Artificial Intelligence (MSc)

disturbedtenAI and Robotics

Jul 17, 2012 (2 years and 5 months ago)

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Contents
1
Artificial Intelligence (MSc) 5
1.1 Knowledge Technology and Intelligent Internet Applications 5
1.1.1 Compulsory courses 5
1.1.2 Strongly recommended optional courses 5
1.1.3 Recommended optional courses 5
1.2 Cognitive Science 6
1.2.1 Compulsory courses 6
1.2.2 Recommended optional courses 6
1.3 Computational Intelligence and Selforganisation 6
1.3.1 Compulsory courses 6
1.3.2 Recommended optional courses 7
1.4 Technical Artificial Intelligence 7
1.4.1 Compulsory courses 7
1.4.2 Compulsory optional course (Software Engineering) 7
1.4.3 Recommend optional courses 7
1.5 AI and Communication 8
1.5.1 Artificial Intelligence part 8
1.5.2 Communication part 8
2
Exam parts 9



Artificial Intelligence (MSc)
4
1
Artificial Intelligence (MSc)
The information presented here concerns the structure of the MSc programme in
Artificial Intelligence and detailed course information. More detailed and general
information about the master programme - for instance the master coordinator,
schedules, etcetera - can be found at
http://www.few.vu.nl/onderwijs/masters/ai
.
1.1 Knowledge Technology and Intelligent Internet Applications
Students must also follow optional courses for a total of 41 cp.
1.1.1 Compulsory courses
Course
code
Course name
Cr.
Period
400113
Behaviour Dynamics
6
1 and 2
400111
Evolutionary Computing
6
1 and 2
400153
Intelligent Web Applications
8
1 and 2
400125
Knowledge Management and Modeling
6
1 and 2
400435
Information Retrieval
6
2
400152
Intelligent Interactive Distributed Systems
8
2 and 3
400290
Qualitative Research Methods for the
Information Sciences
3
3
400285
Master Project Artificial Intelligence
30
4, 5 and 6
400292
Ontology Engineering
3
6
400592
Scientific Writing in English
3
Various dates around the
year, see timetable masters

1.1.2 Strongly recommended optional courses
Course
code
Course name
Cr.
Period
400108
Data Mining Techniques
6
4 and 5
400389
Automated Reasoning in AI
6
5 and 6

1.1.3 Recommended optional courses

Course
code
Course name
Cr.
Period
400010
Bedrijfsmodellering en requirements
engineering
7
1
400440
Multimedia Authoring
6
1
60111030
Management en Organisatie 1.1
3
1
400110
E-Business Innovation
7
1 and 2
400378
Advanced Topics in Software Design
6
1 and 2
400115
Logical Verification
6
1 and 2
400195
Kwaliteitszorg van de
informatievoorziening
5
1 en 2
400029
Inleiding besliskunde
6
1 en 2
400410
Voortgezette logica
4
4
400052
Network Programming
9
4 and 5
400117
Protocol Validation
6
5 and 6
400487
Computernetwerken
6
5 en 6
400067
Project Software Engineering
8
5 en 6
400428
Mini Master Project AI
6
Throughout the year
Artificial Intelligence (MSc) 5

1.2 Cognitive Science
Students must also follow optional courses for a total of 24 cp.
1.2.1 Compulsory courses
Course
code
Course name
Cr.
Period
400113
Behaviour Dynamics
6
1 and 2
400111
Evolutionary Computing
6
1 and 2
400125
Knowledge Management and Modeling
6
1 and 2
400560
Special Topics Cognitive Science
9
1, 2, 3, 4, 5 and 6
815049
Thinking and Deciding (Denken en
Beslissen)
6
2
815096
Behavioral Methods
6
3
815098
Seminar Cognitive Neuroscience
6
4
815067
Master Thesis: Research Project Cognitive
Science
30
4, 5 and 6
815100
Seminar Attention (Seminar Attention)
6
5 and 6
815048
Human Information Processing
(Informatieverwerking)
6
5 and 6
815051
Neural Models of Cognitive Processes
6
2 (in 09/10; not in 08/09)
400592
Scientific Writing in English
3
Various dates around the
year, see timetable masters
1.2.2 Recommended optional courses
Course
code
Course name
Cr.
Period
400054
Design of Multi-Agent Systems
6
1
815103
Brain Imaging
6
1
400434
Advanced Selforganisation
6
2
400154
Machine Learning
6
2
815102
Memory and Memory Disorders
6
2 (in 08/09; not in 09/10)
815104
Review Paper
6
3
815097
Advanced Statistics for Experimentation
6
4
815047
Perception
6
5
400428
Mini Master Project AI
6
Throughout the year

1.3 Computational Intelligence and Selforganisation
Students must also follow optional courses for a total of 44 cp.
1.3.1 Compulsory courses
Course
code
Course name
Cr.
Period
400113
Behaviour Dynamics
6
1 and 2
400125
Knowledge Management and Modeling
6
1 and 2
400111
Evolutionary Computing
6
1 and 2
400310
Design of Experiments and Analysis of
Variance
2
2
400434
Advanced Selforganisation
6
2
400152
Intelligent Interactive Distributed Systems
8
2 and 3
400290
Qualitative Research Methods for the
Information Sciences
3
3
400108
Data Mining Techniques
6
4 and 5
400285
Master Project Artificial Intelligence
30
4, 5 and 6
400592
Scientific Writing in English
3
Various dates around the
year, see timetable masters
Artificial Intelligence (MSc)
6
1.3.2 Recommended optional courses
Course
code
Course name
Cr.
Period
400604
Introduction to Game Theory
6

61312020
Business Intelligence
6
1
400153
Intelligent Web Applications
8
1 and 2
400436
Computational Genomics and Proteomics
6
1 and 2
400073
Statistical Data Analysis
6
1, 2 and 3
400130
Distributed Systems
6
2
400211
Distributed Algorithms
6
4 and 5
430048
Bioinformatic Data Analysis and Tools
6
4 and 5
150005
Inleiding wijsgerige antropologie
6
5
400292
Ontology Engineering
3
6
470053
Evolutionaire genetica
6
01.06.2009-26.06.2009
470622
Intracellular Networks
6
27.10.2008-21.11.2008
470503
Dynamic Energy Budgets
6
february- october 2009
400428
Mini Master Project AI
6
Throughout the year

1.4 Technical Artificial Intelligence
Students must also follow optional courses for a total of 26 cp. Besides these, students
must also choose one course from the compulsory optional courses.
1.4.1 Compulsory courses
Course
code
Course name
Cr.
Period
400132
Neural Networks
6
1
400153
Intelligent Web Applications
8
1 and 2
400111
Evolutionary Computing
6
1 and 2
400125
Knowledge Management and Modeling
6
1 and 2
400130
Distributed Systems
6
2
400154
Machine Learning
6
2
400152
Intelligent Interactive Distributed Systems
8
2 and 3
400290
Qualitative Research Methods for the
Information Sciences
3
3
400285
Master Project Artificial Intelligence
30
4, 5 and 6
400277
Literature Study
6
Variable
400592
Scientific Writing in English
3
Various dates around the
year, see timetable masters
1.4.2 Compulsory optional course (Software Engineering)
one of both
Course
code
Course name
Cr.
Period
400378
Advanced Topics in Software Design
6
1 and 2
400170
Software Architecture
6
2 and 3
1.4.3 Recommend optional courses
Course
code
Course name
Cr.
Period
400435
Information Retrieval
6
2
400410
Voortgezette logica
4
4
400012
C/C++
2
4
400108
Data Mining Techniques
6
4 and 5
400389
Automated Reasoning in AI
6
5 and 6
400428
Mini Master Project AI
6
Throughout the year
Artificial Intelligence (MSc) 7

1.5 AI and Communication
1.5.1 Artificial Intelligence part
This part consists of optional courses and a research project, including a master
thesis. The project and thesis are 21 cp. For the choice of the optional courses (39 cp),
students should consult the master coordinator of the section where they plan to do
their project.

Course
code
Course name
Cr.
Period
400538
Master Project AI for the Communication
Variant
21


1.5.2 Communication part
This part of the programme consists of 60 cp. and is dedicated to Science
Communication. Three courses, one internship or research project and a thesis are
compulsory. The rest of the programme can be filled in with optional courses.

Compulsory courses
An individual Internship of 21 cp. and an individual Thesis of 9 cp. are also
compulsory.
Communication part: compulsory courses
Also compulsory is a individual research project (21 cp) and a individual
thesis (9 cp).
Course
code
Course name
Cr.
Period
470582
Qualitative and Quantitative Research
Methods
6
01.09.2008-26.09.2008
470587
Science and Communication
6
05.01.2009-30.01.2009

Recommended optional courses
Communication part: optional courses
At least 12 cp are required.
Course
code
Course name
Cr.
Period
470087
Gezondheidscommunicatie
6
01.06.2009-26.06.2009
470562
Interactive Communication
3
13.10.2008-24.10.2008
471026
Museologie en buitenschoolse educatie
6
24.11.2008-19.12.2008
471014
Wetenschapsjournalistiek (science
journalism)
6
27.10.2008-21.11.2008
471007
Interpersoonlijke communicatie
3
29.09.2008-10.10.2008
470572
Communication, Organization and
Management
6
29.09.2008-24.10.2008


Artificial Intelligence (MSc)
8
2
Exam parts
subject
Advanced Selforganisation
code
400434
lecturer
dr. M.C. Schut
credits
6
period
2
aim
To understand, simulate and analyse the behaviour and self-organization of
complex systems. The student is able to explain, implement and recognize
basic principles and properties of such systems.
content
This course is about the understanding of the behavior and self-organization
of complex systems: systems in which the interaction of the components is
not simply reducible to the properties of the components. The general
question the we address is: how should systems of very many independent
computational (e.g., robotic or software) agents cooperate in order to process
information and achieve their goals, in a way that is efficient, self-
optimizing, adaptive, and robust in the face of damage or attack? We will
look at natural systems that solve some of the same problems that we want to
solve, e.g., adaptive path minimization by ants, wasp and termite nest
building, army ant raiding, fish schooling and bird flocking, coordinated
cooperation in slime molds, synchronized firefly flashing, evolution by
natural selection, game theory and the evolution of cooperation. The course
includes a practical part in which students implement a simulation of a self-
organizing complex system and conduct structured experimental analysis
with this simulation.
form of tuition
Theory in lectures and practice in labs.
literature
Schut M.C., Scientific Handbook for Simulation of Collective Intelligence,
2007. Available at
http://www.sci-sci.org/
.
mode of assessment
Report including description of simulation and experimental analysis.
target audience
mAI (computational intelligence and self organisation), mBMI, mIS, mCS
remarks
More information available on BlackBoard. This is a project-oriented course
and therefore students will be expected to have basic programming skills.

subject
Advanced Statistics for Experimentation
code
815097
credits
6
period
4
lecturer
dr. N. Smits
aim
To acquire knowledge of and insight into multivariate statistics in order to be
able to apply these techniques and read associated literature at a level
relevant for research in cognitive neuropsychology
content
Multivariate Statistics: the General Linear Model.
form of tuition
Lectures and practicals
literature
To be announced
mode of assessment
Assignments and final examination
remarks
Admission conditions: Statistics and Research methods II (or a similar
course).


Exam parts 9

subject
Advanced Topics in Software Design
code
400378
lecturer
dr. P. Lago
credits
6
period
1 and 2
aim
Learn advanced design techniques applicable to large software systems. Be
able to select among them and apply them for a specific system. Be able to
document and compare the design decisions.
content
The lectures explain the most innovative design techniques. Examples are:
service-oriented design, domain design and product line/family engineering,
pattern-oriented design, web design, global software development.
The students work in small groups to discuss the different design techniques
and how to use them for an assigned software system. They have to develop
different representations of the system. Each representation has to emphasize
how a certain design technique has been applied, and the pros and cons it
brings in the developed solution. Each representation constitutes a design
documentation for the software system.
form of tuition
Lectures and group work.
literature
Material handed out by the lecturer and on Blackboard.
mode of assessment
Written reports of the assignment. Teamwork.
entry requirements
Basic knowledge on Software Engineering theory and practice.
target audience
mCS, 3IMM, mIS, mBMI, mAI
remarks
Registration for this course is compulsory in TIS via
https://tisvu.vu.nl/tis/menu
, two weeks prior to the start.
Further information on this module will be made available on the Blackboard
system
http://bb.vu.nl
.

subject
Automated Reasoning in AI
code
400389
docent
dr. A.C.M. ten Teije
lecturer
prof.dr. F.A.H. van Harmelen
credits
6
period
5 and 6
aim
Since its early days Artificial Intelligence has employed logic as a mean to
provide generic solutions for computationally and conceptually difficult
practical problems.
The aim of the course is to make the students familiar with a number of
popular logic-based representation and reasoning mechanisms for Artificial
Intelligence. Furthermore, students should have the capability to transfer the
learned techniques to other problems and to other representation
mechanisms.
content
The course will be structured in three modules. In each of these modules a
practical problem will be introduced, a logic-based representation proposed,
and the basic techniques for automated reasoning in this language studied in
a practical, hands on, way.
In a nutshell, we plan to cover:

propositional Logic for scheduling, and satisfiability checking with Davis
Putnam;

Allen's interval logic for Planning, with constraint propagation in
Temporal Constraint Networks;
Artificial Intelligence (MSc)
10

description logics for classification, with Tableau calculi for
subsumption.
form of tuition
In period 5 there will be lectures and practical sessions, plus significant time
for self-study and practical work. In period 6 there will be regular meetings
to support for the work on a larger project.
literature
Selected scientific papers.
mode of assessment
3 practical assignments (2 in period 5, 1 in period 6).
entry requirements
Basic knowledge in logic is an advantage, but not required, as is some
familiarity with programming.
target audience
Master AI, in particular the specialization "Knowledge Technology and
Intelligent Internet Applications".
remarks
For further information see the AR in AI blackboard site.

naam
Bedrijfsmodellering en requirements engineering
code
400010
docent
dr. J.F.M. Burg
studiepunten
7
periode
1
doel
Na dit vak is de student in staat:

een probleem- en veranderingsanalyse uit te voeren met betrekking tot
een IT vraagstuk in een bedrijfsmatige context;

op modelmatige wijze in kaart te brengen hoe een informatiesysteem als
oplossing past in bedrijfsstrategie en bedrijfsproces;

verschillende methodieken toe te passen voor het eliciteren van door de
organisatie te stellen eisen aan een te ontwikkelen informatiesysteem.
inhoud
Het vak BedrijfsModellering en Requirements Engineering (BMRE)
behandelt de analyse van bedrijfsvraagstukken, waarbij introductie of
uitbreiding van een informatiesysteem een van de mogelijke oplossingen is.
Dit omvat de activiteiten en methodieken die nodig zijn om:
(1) een probleemanalyse uit te voeren met betrekking tot IT vraagstukken in
een bedrijfsmatige context;
(2) te modelleren hoe een gewenst informatiesysteem past in het
bedrijfsproces en aan te geven welke eventuele veranderingen daarbij
wenselijk zijn;
(3) het ontwikkelen en toetsen van het te stellen pakket van eisen aan een te
bouwen informatiesysteem.
werkwijze
Het vak bestaat uit een college met een tentamen en een practicum. Beide
moeten voldoende zijn.
literatuur
Syllabus.
toetsing
Tentamen plus practicumverslag.
doelgroep
2IMM, 3I, 3BWI

subject
Behavioral Methods
code
815096
credits
6
period
3
lecturers
dr. M.R. Nieuwenstein; dr. L.J.F.M. van Zoest
aim
This course aims to provide students with knowledge of commonly used
methods in cognitive psychology, and their problems. This will include
methods for data collection, data analysis, and theory development.
Exam parts 11

content
Examples of methods that will be covered in this course are signal detection
theory, the methods used in cognitive neuropsychology (e.g., double
dissociations), psychophysical experiments (e.g., how to obtain a reliable
estimate of a sensation threshold), and memory research (e.g., the process-
dissociation technique). The course also covers the problems of some
commonly used methods in data-analysis (e.g., null-hypothesis significance
testing, interpretation of p values), and the principles that govern the
development and evaluation of theories (e.g., philosophy of science,
hypothesis testing).
form of tuition
Lectures
literature
A selection of articles and bookchapters.
mode of assessment
open-ended written examination

subject
Behaviour Dynamics
code
400113
lecturers
dr. T. Bosse; dr. M. Hoogendoorn
credits
6
period
1 and 2
content
This course teaches analysis and modelling of the dynamics of behaviour in
Artificial, Biological, Cognitive and Social systems. Behavioural dynamics
occurs in different forms, contexts and complexity. During the course
examples of such behaviour are studied coming from software systems (e.g.,
knowledge- and agent-based systems), cognition (e.g., the use of beliefs,
desires and intentions, complex reasoning tasks) and organisation theory
(e.g., organisational change). The dynamics of behaviour of such systems is
analysed (including verification and validation), modelled and simulated in
this course using different techniques and tools.
form of tuition
Combinations of lectures, practical assignments, and presentations.
literature
Online reader.
mode of assessment
Examination and practical assignments. Both grades should be at least 5.5 to
pass the course.
target audience
mAI

subject
Bioinformatic Data Analysis and Tools
code
430048
coördinatoren
prof.dr. J. Heringa; dr.ir. K.A. Feenstra
lecturers
prof.dr. J. Heringa; prof.dr. F.A.H. van Harmelen; dr. T. Kielmann;
dr.ir. K.A. Feenstra
credits
6
period
4 and 5
aim
A theoretical and practical bioinformatics course on the fundamentals of
bioinformatics tools and tool creation for biological data mining.
Goals:

At the end of the course, students will be aware of the issues,
methodology and available bioinformatics tools, so to become a creative
bioinformatics problem solver and tools creator.

At the end of the course, students will have hands-on experience in
statistical thermodynamics and clustering techniques.
content
Theory:

Microarray and array-CGH data, introduction to statistical
Artificial Intelligence (MSc)
12
thermodynamics of soft and biological matter, molecular mechanics
simulations and sampling, repeat recognition tools and concepts (e.g.
transitivity), protein domain prediction concepts and tools, pattern
recognition (clustering techniques), machine learning techniques, genetic
algorithm, ontologies, semantic web, parallel computational techniques
and GRID computing
Practical:

Assignment statistical thermodynamics

Assignment biological data clustering
form of tuition

13 Lectures (2 two-hour lectures per week)

Assignment introductions

Computer practicals

Hands-on support
Oral lectures, active participation, on-line assignments, assignment
inductions and consultation (one-to-one teaching)
literature

E-course material (slides, assignment material, papers):
http://ibi.vu.nl


Books: Nelson, P.,
Biological Physics. Energy, Information, Life
. W H
Freeman & Co., July 2003, 600 pages, ISBN: 0716743728.
mode of assessment
Assignment results and oral or written exam (depending on number of course
students).
entry requirements
A completed course Sequence Analysis and DNA/Protein Structure-Function
Analysis and Prediction is a strong advantage. Some experience in
programming is required.
target audience
MSc Bioinformatics, Students with Bachelor degree in Physics, Chemistry,
Mathematics, Computer Science, Biology, Medical Natural Sciences or
Medicine, with a strong interest and some basic knowledge in
Bioinformatics.
remarks
The course is taught in English.

subject
Brain Imaging
code
815103
credits
6
period
1
lecturer
dr. D.J. Heslenfeld
aim
To learn how various brain imaging techniques are used in modern neuro-
cognitive research.
content
The course will treat physical principles, recording apparatus, and practical
applications of the four major brain imaging techniques: EEG, MEG, MRI,
PET, with an emphasis on EEG and MRI. These techniques will be discussed
in detail and live demonstrated. We will visit the various labs, and students
will perform a small research project of their own. This includes recording,
analyzing and presenting your own brain imaging data in a small supervised
group.
form of tuition
Lectures and obligatory practicals.
literature
To be announced
mode of assessment
Written report, oral presentation, contribution to practicals.
entry requirements
Cognitive Neuroscience and Neuropsychology.
remarks
Language: tuition in English
MRI practicals will take place on Wednesdays in the afternoon/evening

Exam parts 13

subject
Business Intelligence
code
61312020
credits
6
contact
18 hours (6 tutorial, 12 lecture)
period
1
co-ordinator
dr. J.F.M. Feldberg
lecturers
prof.dr. A.E. Eiben; dr. J.F.M. Feldberg
aim
The primary aim of this course is to establish an elementary frame of
reference concerning business intelligence. Despite the fact that the course
focus is primarily managerial and not technical, an important objective is to
train students in the successful application of a popular decision support tool
(Cognos Powerplay). By means of 'learning by doing' elementary skills in
the usage of decision support systems are acquired. Students completing this
course successfully, will be able to actively collaborate in sensible thinking
and deciding about the benefits, development, application, and
implementation of business intelligence solutions. The realization of business
objectives and sustainable competitive advantage are keywords in this
context. In addition to this, the frame of reference offers a point of departure
for further self-study to deepen and broaden the knowledge offered.
content
Modern organizations, in particular the management of these organizations,
tend to suffer more from an overload of data than from a lack of data. To a
great extent this overload is caused by the overwhelming growth of
information systems in organizations. Enterprise Systems (ERP), Customer
Relationship Systems (CRM) as well as the growing number of Internet-
based applications (e.g. e-commerce) are all important sources for the
explosion of financial, production, marketing and other business data. The
challenge for most organizations is to develop and build systems that support
the transformation of the collected data into knowledge. To be successful in
this transformation processes organizations have to develop the capability to
aggregate, analyze and use data to make informed decisions. This course
deals with the theory concerning business intelligence as well as with the
application of business intelligence solutions. To be able to successfully
implement business intelligence solutions, one has to have knowledge about
their functioning and proficiency in using them, as well as knowledge about
their field of application, e.g., how to select, transform, integrate, condense,
store and analyze relevant data. This course uses the term 'business
intelligence' in a broad sense. A narrow interpretation would only deal with
software solutions ('data warehousing' and 'online analytical processing').
The broad interpretation - to be used in this course - also includes: theories
concerning decision making, related decision support systems and their
application for management, i.e., data warehousing, online analytical
processing and data mining.
literature

Book (to be announced)

Various papers.
examination format
written interim examination
65 percent
practical test
(weekly) business intelligence tutorial tests (35 percent). All tests and exams
will be administered through a digital test environment.
recommended

Basic course in Information Systems, f.e. on the level of Laudon &
Artificial Intelligence (MSc)
14
background knowledge
Laudon, Management Information Systems, Managing the Digital Firm.
9th edition.Prentice Hall, 2004.

O'Brien, James A., Introduction to Information Systems. 12th edition. Mc
Graw Hill, 2005.

naam
C/C++
code
400012

Het vak wordt geven in de eerste 4 weken van periode 4.
docent
dr. N. Silvis-Cividjian
studiepunten
2
periode
4
doel
Het verwerven van basiskennis in C en C++, nodig voor o.a. het schrijven
van computersimulaties
inhoud
Het college is een korte introductie van C/C++ als tweede programmeertaal.
Ervaring met een andere programmeer taal zoals Java is vereist. Een paar
belangrijke programmeeraspecten worden tijdens het college besproken en in
kleine opdrachten wekelijks geoefend. Drie verschillende manieren van
programmeren in C++ worden belicht: procedureel, object georienteerd en
generiek programmeren.
Topics : C/C++ basic data types, arrays, strings, functions, file I/O, pointers,
linked lists, classes, separate compilation, templates, generic algorithms, the
standard template library (STL).
werkwijze
4 hoorcolleges en 4 verplichte programmeeropdrachten.
literatuur
Stephen Prata,
C++ Primer Plus
, SAMS, 2005. Website met nuttige links en
documentatie is beschikbaar via Blackboard
toetsing
Op basis van de verplichte programmeeropdrachten.
doelgroep
2BWI, 3Ect
voorkennis
Vereist voor deelname: Inleiding programmeren II practicum (400085) of
Inleiding programmeren practicum voor Ect (400200).

subject
Communication, Organization and Management
code
470572
co-ordinator
dr. M.B.M. Zweekhorst
lecturers
dr. M.B.M. Zweekhorst; prof.dr. C.J. Hamelink; drs J. Maas; others
credits
6
period
29.09.2008-24.10.2008
aim

To get acquainted with communication theories

To obtain in-depth understanding on communication from the perspective
of sharing and influencing results

To acquire knowledge on organizational structures and designs

To get acquainted with important theories on organizational structures
(e.g. Mintzberg)

To acquire insight into different management practices in the health and
lifescience sector;

To obtain insight in motivation methods and conflict management

To gain insight and to practice leadership

To improve communication skills

To practise team management
content
Organizations in the health and life science sector are fast changing in part by
newly emerging technologies and increasing societal complexity. A growing
Exam parts 15

number of students with a beta degree become managers/professionals in
these organizations. During this course students learn how to be effective
performers both individually and in teams within organizations. This requires
understanding the macro aspects of organizational behavior, which of
necessity involves managerial skills and ways of strategic thinking. Several
speakers conduct lecturers on different aspects, such as motivation, managing
behavior between people, leadership, communication and developing and
changing of organizations. The speakers will explain theories from literature
and relate the theories to the experiences from practice. In addition, the
students become a project manager of a project team (second year course
`Biomedisch Beleid en (Kennis)management¿ of `Van Gen tot Gewas¿) that
has been given the assignment to write a policy advisory report. While being
a project manager you are trained and coached by experts. With the other
students you discuss your experiences and the coach helps you relate the
experiences to theory.
form of tuition
Lectures, self study, training workshops project assignment
literature
"Management and organizational behaviour", Wendy Bloisi (European
edition), McGraw-Hill Education, ISBN 0-07-709945-1
mode of assessment
Written exam and assessment of the functioning as a team manager. Note
both parts need to be passed
target audience
Compulsory course within the Masterprogramme Management, Policy
Analysis and entrepreneurship for the health and life sciences (MPA) and the
Societal differentiation of Health, Life and Natural Sciences Masters
programmes
remarks
Attendance to trainingworkshops and project are compulsory.

subject
Computational Genomics and Proteomics
code
400436
lecturer
prof.dr. J. Heringa
credits
6
period
1 and 2
aim
The course provides an insight into methods and algorithms for genomics
and for proteomics data analysis. The course is aimed at students with an
exact sciences background. At the end of the course students will be familiar
with the basic principles of analysing the human genome and high-
throughput proteomics data.
content
The course is structured around the following main topics:
Biology: An introduction to molecular biology and genome biology, lectures
explaining principles of biology required for the course. No additional
biological knowledge expected!
Sequences: Sequence comparison, searching large amounts of biological
data, detecting genes and motifs
Genomes: Sequencing and assembling, genome duplication, rearrangements,
evolution, comparative genomics, genome repeats
Proteomics: High-throughput mass spectrometry data, biomarker detection,
computational diagnostics
Protein-protein interaction (PPI): interaction networks, mesoscopic
modeling, docking
form of tuition
Lectures and assignments.
literature
Course materials and references are available at the Centre for Integrative
Artificial Intelligence (MSc)
16
Bioinformatics (IBIVU) website:
http://www.ibivu.cs.vu.nl/teaching/

mode of assessment
Written exam and assignments
entry requirements
Writing algorithms in pseudocode; Mathematical skills.
target audience
Third and fourth year students of CS, AI, Math, Physics.
remarks
The course will only take place if at least 10 students register within the
required notice period. The course is taught in English.

naam
Computernetwerken
code
400487
docent
dr.ir. H.J. Bos
studiepunten
6
periode
5 en 6
doel
Het inzichtelijk maken van de architectuur van computernetwerken.
inhoud
De nadruk ligt op het behandelen van de architectuur van
communicatieprotocollen, zowel voor hoog- als
laagniveau-communicatie. Onderwerpen die aan de orde komen zijn: de
fysieke laag, de datalinklaag, de netwerklaag, de transportlaag en de
applicatielaag. Voorbeelden die aan de orde komen zijn onder meer het
Internet, Internet via de kabel, en draadloze netwerken. Aandacht
wordt ook besteed aan beveiligen van netwerken.
werkwijze
Hoorcollege.
literatuur
Tanenbaum, A.S.,
Computer Networks
4th edition. Prentice-Hall, 2003.
toetsing
Schriftelijk.
doelgroep
2I, 2IMM
voorkennis

Inleiding Computersystemen OF

Pervasive Computing
opmerkingen
Actuele informatie over het vak is te vinden op:
http://www.cs.vu.nl/~steen/courses/cn.html

subject
Data Mining Techniques
code
400108
lecturer
dr. W.J. Kowalczyk
credits
6
period
4 and 5
content
The course will provide a survey of basic data mining techniques and their
applications for solving real life problems. After a general introduction to
Data Mining we will discuss some "classical" algorithms like Naive Bayes,
Decision Trees, Association Rules, etc., and some recently discovered
methods like boosting, Support Vector Machines, co-learning. In the second
part of the course a number of most successful applications of data mining
will be discussed: marketing, fraud detection, text and Web mining,
bioinformatics. In addition to lectures there will be an extensive practical
part, where students will experiment with various data mining algorithms and
data sets. The grade for the course will be based on these practical
assignments (i.e., there will be no final examination).
form of tuition
Lectures and compulsory practical work.
literature
Ian H. Witten, Eibe Frank,
Data Mining: Practical Machine Learning Tools
and Techniques with Java Implementations
, Morgan Kaufman, 2000.
Additionally, a collection of articles in electronic form.
mode of assessment
Computerpracticum.
Exam parts 17

entry requirements
Vereist voor deelname aan het tentamen: Kansrekening en Statistiek of
Algemene Statistiek. Aanbevolen: Machine Learning.
target audience
mBMI, mCS, mAI

subject
Design of Experiments and Analysis of Variance
code
400310
lecturer
prof.dr. A.W. van der Vaart
credits
2
period
2
aim
Learn how to design experiments and analyse the results by ANOVA. Not
only in theory, but also in practice using a statistical package.
content
In order to draw sound conclusions from an experiment or survey it is
necessary that the study is well designed. In this course a few well known
designs (completely randomized, randomized block etc.) and the associated
analyses of variance are discussed.
form of tuition
Classes.
literature
Lecture notes and slides that can be found via
http://www.math.vu.nl/sto/onderwijs/doeanova/
mode of assessment
Exercises, final project with oral examination.
entry requirements
Descriptive statistics (comparable to third year course "toegepaste statistiek")
target audience
mIS, mCS
remarks
Homework consists of computer exercises, to be solved using the statistical
package R (
http://www.r-project.org/
). Classes are in English.

subject
Design of Multi-Agent Systems
code
400054
lecturer
dr. M. Hoogendoorn
credits
6
period
1
content
This course discusses the design techniques of knowledge-
b
ased systems that
consist of various intelligent agents and centers around the notion of
compositional architecture. The design method used is DESIRE. A number
of examples of agent models and generic task models are treated. In the
associated practical work in spring, hands on experience is gained in the
design of compositional multi-agent and knowledge systems using DESIRE
tools.
form of tuition
Combination of lectures and practical assignments.
literature
Reader.
mode of assessment
On the basis of the homework assignments, practical assignments and a
written exam.
entry requirements
Kennissystemen (400126) and Logische taal en redeneermethoden (400043).
target audience
3AI, 3I, mCS
remarks
More information can be found on Blackboard.

subject
Distributed Algorithms
code
400211
lecturer
prof.dr. W.J. Fokkink
credits
6
period
4 and 5
aim
To obtain a good understanding of concurrency concepts and a large range
Artificial Intelligence (MSc)
18
of distributed algorithms.
content
Snapshots, traversal algorithms, termination detection, routing
algorithms, deadlock-free packet switching, leader election, minimal
spanning trees, anonymous networks, fault tolerance, failure detection,
synchronization, mutual exclusion, garbage collection, scheduling.
form of tuition
Lectures and exercise classes.
literature

Gerard Tel,
Introduction to Distributed Algorithms
(2nd edition).
Cambridge University Press, 2000.

Hagit Attiya and Jennifer Welch,
Distributed Computing: Fundamentals,
Simulations and Advanced Topics
(chapter 4). McGraw-Hill, 1998.

Jane Liu, Real-Time Systems. Prentice Hall, 2000.
mode of assessment
Written examen (plus a home exercise sheet that can provide up to 0,5 bonus
point).
target audience
mCS, mPDCS

subject
Distributed Systems
code
400130
lecturer
prof.dr.ir. M.R. van Steen
credits
6
period
2
aim
After taking this course, the student will have gained insight in the
design and implementation of modern distributed systems, and notably
the trade-offs that need to be considered between making design
decisions.
content
We discuss the issues concerning the development of middleware systems
for large-scale computer networks. Principles that are discussed
include architecture, processes, communication, naming,
synchronization, consistency and replication, fault tolerance, and
security. These principles are further explained by means of different
paradigms applied to distributed systems: object-based systems,
distributed file systems (NFS), Web-based systems, and
coordination-based systems (publish/subscribe systems). Explicit
attention is paid to the practical feasibility and scalability of
various solutions. For this reason, experimental (research) systems as
well as commercially available systems are discussed.
form of tuition
Lectures.
literature
Tanenbaum, A.S., Steen, M. van,
Distributed Systems,Principles and
Paradigms
2nd edition. Prentice-Hall, 2007.
mode of assessment
Written exam.
entry requirements
Computer Networks (Computernetwerken, code 400016).
target audience
mCS, mPDCS
remarks
More information, slides and relevant literature, can be found in Blackboard.

subject
Dynamic Energy Budgets
code
470503
co-ordinator
prof.dr. S.A.L.M. Kooijman
lecturer
prof.dr. S.A.L.M. Kooijman
credits
6
period
february- october 2009
content
A quantitative theory for processes of energy uptake and use by organisms is
Exam parts 19

discussed. For more information see
http://www.bio.vu.nl/thb/deb/course/deb
.
form of tuition
Tele-course, form of tuition to be discussed with the course co-ordinator.
literature
See
http://www.bio.vu.nl/thb/deb/course/deb
mode of assessment
Software package DEBtool will be used to exercise the practical application
of the DEB theory.
target audience
Master and PhD students in natural sciences & mathematics.
remarks
For more information see
http://www.bio.vu.nl/thb/deb/course/deb

subject
E-Business Innovation
code
400110
lecturers
dr.ing. J. Gordijn; drs. E. Schulten
credits
7
period
1 and 2
aim
To understand and systematically analyze a business model for an innovative
e-business idea. To develop and present an e-business plan with the goal to
attract venture capital.
content
We will introduce a methodology called e3value for understanding and
analyzing business models for networked value constellations. Additionally,
we discuss how to write a business plan for venture capitalists.
form of tuition
Combination of lectures, topical workshops and project.
literature
Reader.
mode of assessment
On the basis of an e-business project, workshops and a written exam.
entry requirements
Advance knowledge equivalent to Bedrijfsmodellering en requirements
engineering (400010) and Software Engineering (400071) is recommended.
target audience
3IK, mIS, mCS, mBMI

naam
Evolutionaire genetica
code
470053
coördinator
dr. J.M. Kooter
docenten
dr. H. Schat; dr. J.M. Kooter; dr. D. Roelofs
studiepunten
6
periode
01.06.2009-26.06.2009
doel
Verwerven van kennis en inzicht in :

dynamische karakter van genetisch materiaal en genetische variatie

oorzaken genetische variatie op nucleotide, gen, en chromosoom-niveau

genoomevolutie bij pro- en eukaryoten

vergelijkende genomics

evolutionaire gevolgen van sex

ecologische en moleculaire oorzaken van soortvorming

horizontale DNA overdracht

gebruik van genomische databanken bij evolutiestudies

modellen van de moleculaire oorsprong van leven op aarde

reconstructie van fylogenetische bomen met behulp van het
computerprogramma PAUP

verschillende vormen van selectie en theoretische onderbouwing

manieren waarbij genetische variatie wordt gebruikt om oorzaken van
stochastische en deterministische processen af te leiden

toepassing van wiskundige regels die bestaan voor het gedrag van allelen
van één of twee loci in ideale populaties, en voor genen met een
Artificial Intelligence (MSc)
20
kwantitatief effect

de relatie tussen ziekte en evolutie

moleculaire evolutie van pathogenen (bacterien, virussen, protozoa)
Niveau 2: verdieping
inhoud
De cursus behandelt:

Genetische concepten die de basis vormen voor het begrijpen van de
evolutietheorie, waaronder moleculaire evolutie, ontstaan van nieuwe
genen en functies, genoom organisatie, vergelijkende genomics,
soortvorming, humane genoom evolutie, relatie ontwikkeling en evolutie,
en hypothesen over het ontstaan van `leven',

Theoretische principes van de populatie genetica, waaronder
verschillende vormen van selectie, quantitatieve genetica, drift, en hun
toepassingen bij het bestuderen van variatie en evolutie in natuurlijke
populaties.

Fylogenetische reconstructies op basis van DNA sequenties met behulp
van een cladistisch computerprogramma

Fylogeografie
werkwijze

Hoorcolleges (40 uur)

Werkcolleges (verplicht, 20 uur)

Literatuurbespreking (verplicht, 9 uur)

Computer Practicum (verplicht, 9 uur)

Zelfstudie

Ondersteuning via Blackboard
literatuur

Studieboek: 'Evolutionary Analysis', Scott Freeman and Jon C. Herron,
Fourth Edition, 2007, Pearson, Prentice Hall

Syllabus met Onderzoeks- en Reviewartikelen over onderwerpen die in
het boek niet worden behandeld
toetsing
Schriftelijk tentamen (0.8) en een literatuurbespreking (0.2). Beide moeten
voldoende zijn.
doelgroep
Keuzevak voor derdejaars bachelorstudenten Biologie en Bio-medische
Wetenschappen.
voorkennis
Genetica, Evolutie van de mens of Evolutiebiologie uit het eerste jaar.
opmerkingen
De cursus wordt gegeven door de afdelingen Genetica, Ecologie en
Fysiologie van planten, en Dierecologie.

subject
Evolutionary Computing
code
400111
lecturer
prof.dr. A.E. Eiben
credits
6
period
1 and 2
aim
To learn about computational methods based on Darwinian principles of
evolution. To illustrate the usage of such methods as problem solvers and as
simulation, respectively modelling tools.To gain hands-on experience in
performing experiments.
content
The course is treating various algorithms based on the Darwinian evolution
theory. Driven by natural selection (survival of the fittest), an evolution
process is being emulated and solutions for a given problem are being "bred".
During this course all "dialects" within evolutionary computing are treated
(genetic algorithms, evolutiestrategieën, evolutionary programming, genetic
programming, and classifier systems). Applications in optimisation,
Exam parts 21

constraint handling and machine learning are discussed. Specific subjects
handled include: various genetic structures (representations), selection
techniques, sexual and asexual genetic operators, (self-)adaptivity. If time
permits, subjects in Artificial Life and Artificial Societies, and Evolutionary
Art will be handled. Hands-on-experience is gained by a compulsory
pogramming assignment.
form of tuition
Oral lectures and compulsory pogramming assignment.
literature
Eiben, A.E., Smith, J.E.,
Introduction to Evolutionary Computing
. Springer,
2003 ISBN 3-540-40184-9.
Slides available from
http://www.cs.vu.nl/~gusz/ecbook/ecbook.html
.
mode of assessment
Written exam and pogramming assignment (weighted average).
target audience
mBMI, 3AI, mAI, mCS, mPDCS

naam
Gezondheidscommunicatie
code
470087
coördinator
dr. J.E.W. Broerse
docenten
dr M. Adriaanse; Gastdocenten; dr. E.W.M.L. de Vet; dr. J.E.W. Broerse
studiepunten
6
periode
01.06.2009-26.06.2009
doel

Inzicht krijgen in de centrale begrippen rond het communiceren van
gezondheidsboodschappen naar de hele samenleving of specifieke
doelgroepen

In staat zijn een planningsmodel toe te passen op een concreet voorbeeld
en de valkuilen te onderkennen in de planning van
gezondheidscommunicatie.

In staat zijn het belang van de analyse van gezondheidsproblemen voor
de planning van gezondheidscommunicatie te onderkennen, op te kunnen
stellen en de uitkomsten te interpreteren.

In staat zijn de gereedschappen van de voorlichter en de daarbij passende
literatuur te beschrijven en toe te passen op een concreet voorbeeld.

In staat zijn de uitkomsten van een gedrags- en
omgevingsfactorenanalyse van een gezondheidsprobleem te interpreteren
en te verwerken in een plan van aanpak middels
gezondheidscommunicatie.
Niveau 2: Verdieping
inhoud
In deze cursus worden de definities, concepten en theorieën rondom
gezondheidscommunicatie en gedrag uiteengezet, alsook een aantal
specifieke vormen van (gezondheids)communicatie (persuasief, informatief
en educatief), doelgroepen en kanalen (media; zoals TV, posters, etc.). Naast
het bieden van een theoretisch kader is deze cursus gericht op de praktische
toepasbaarheid. In het kader van een specifiek gezondheidsprobleem maak je
met twee/drie medestudenten een probleemanalyse, definieer je de
doelgroep, maak je een gedrags- en omgevingsfactorenanalyse en bedenk je
(op basis van de voorgaande analyses) een communicatiestrategie.
werkwijze
Hoorcolleges, werkcolleges, (groeps)opdrachten en zelfstudie
literatuur
Syllabus en aanvullende literatuur bij de colleges
toetsing

Beoordeling van de opdracht (drie deelopdrachten plus een presentatie):
40 procent van het eindcijfer.

Schriftelijk tentamen (multiple choice en open vragen): 60 procent van
het eindcijfer.
Artificial Intelligence (MSc)
22
Voor zowel de opdracht, als het tentamen dient een voldoende behaald te
worden!
doelgroep
Keuze voor derdejaars studenten BSc Algemene
Gezondheidswetenschappen, derdejaars studenten BSc Gezondheid en
Leven, en masterstudenten in 1 van de bètaopleidingen in de C-specialisatie
(wetenschapscommunicatie).
De cursus wordt ten zeerste aanbevolen voor bachelorstudenten die de
masterspecialisatie Preventie en gezondheid willen gaan volgen.
opmerkingen
Aanwezigheidsplicht: iedere student moet bij de opdracht minimaal eenmaal
presenteren en mag maximaal eenmaal afwezig zijn bij de werkcolleges.
Maximaal 90 deelnemers.

subject
Human Information Processing (Informatieverwerking)
code
815048
credits
6
period
5 and 6
lecturer
dr. S.A. Los
aim
At the end of this course students should be capable of:

outlining some major theories and controversies in human information
processing, in particular relating to the concepts of processing stages, the
central bottleneck, and executive control;

specifying major methodological approaches to these controversies;

deriving experimental predictions from research hypotheses and theories;

interpreting results from research in terms of theoretical constructs;

discussing interrelations among different theories in human information
processing.
content
One or two research articles are covered during each lecture. The emphasis
will be on (1) distinguishing the research hypotheses and underlying
assumptions; (2) the experimental approach to test the hypotheses and (3)
how the data bear on the different hypotheses.
form of tuition
Lectures
literature
A series of journal articles to be specified at the first lecture.
mode of assessment
Open-ended written examination.
remarks
Basic knowledge of experimental methods is assumed.

subject
Information Retrieval
code
400435
lecturers
dr L. Aroyo; dr. K.S. Schlobach; dr. V. Malaise
credits
6
period
2
aim
The aim of this course is to introduce the basic concepts of Information
Retrieval, and to give students the knowledge to adopt and apply existing
Information Retrieval tools for practical applications.
content
Information Retrieval is the discipline of providing access to information
stored in textual documents within a large collection. In the course, we
introduce the basic concepts of Information Retrieval, including
representation of documents, retrieval models and algorithms for clustering
and classification.
form of tuition
2 hours lecture, and 2 hours practical sessions per week, in a period of 7
weeks, plus a significant time for practical work.
Exam parts 23

mode of assessment
3 practical assignments.
entry requirements
Programming skills will be an advantage.
target audience
Master AI, in particular the specialization "Knowledge Technology and
Intelligent Internet Applications", and the Master "Information Science".

naam
Inleiding besliskunde
code
400029
studiepunten
6
periode
1 en 2
docent
prof.dr. H.C. Tijms
doel
Kennis en inzicht hebben in het opstellen van OR-modellen en hun
oplossingsmethoden.
inhoud
In deze inleiding zal het accent liggen op het opstellen van wiskundige
modellen voor diverse optimaliseringsproblemen. Daarnaast worden de
basisideeën besproken van de wiskundige technieken waarmee deze
modellen worden doorgerekend. In het college is het gebruik van educatieve
software voor het oplossen van de wiskundige optimaliseringsproblemen
geïntegreerd. De volgende onderwerpen komen aan de orde:

lineaire programmering (modelformuleringen, simplex algoritme,
schaduwprijzen, gevoeligheidsanalyse);

netwerkanalyse (kortste-pad algoritme, minumum-opspannende boom,
Steiner probleem);

geheeltallige en combinatorische optimalisering (modelformuleringen, 0-
1 variabelen, branch-en -bound methode, heuristieken);

sequentiele beslissingsproblemen (dynamische optimalisering);

vooraadtheorie (de EOQ-formule, Silver-Meal heuristiek).
werkwijze
Hoorcollege: 2 uur per week; practicum: 2 uur per week.
literatuur
Tijms, H.C.,
Inleiding in de Operationele Analyse
tweede druk. Utrecht:
Epsilon, 2004.
toetsing
Via twee deeltentamens aan het einde van periode 1 en periode 2.
doelgroep
1BWI, 1W

naam
Inleiding wijsgerige antropologie
code
150005
docent
dr. L.D. Derksen (kamer 13A-40, tel. (020) 59 86684, e-mail
ld.derksen@ph.vu.nl)
studiepunten
6
periode
5
doel
Dit college is bedoeld als een eerste oriëntatie in wijsgerige theorieën over de
mens en als een introductie tot theoretische benaderingen van de wijsgerige
antropologie.
Op dit college krijg je inzicht in de lichaam-geestproblematiek door teksten
te lezen op het gebied van de hedendaagse philosophy of mind. Je maakt
kennis met de ideeën van vooraanstaande auteurs op dit gebied en met
actuele discussies over dit onderwerp. Ook oefen je vaardigheden, zoals het
geven van een inleiding op een tekst tijdens een college, het opzoeken van
achtergrondinformatie, het formuleren van filosofische vragen, en het
beargumenteren van een eigen standpunt over kwesties die worden
besproken.
inhoud
Het boek Theories of Mind is een verzameling van klassieke artikelen op het
Artificial Intelligence (MSc)
24
gebied van philosophy of mind. Auteurs die aan de orde komen zijn o.a.
Descartes, Ryle, Fodor, Smart, Turing, Churchland, Dennett, Searle en
Nagel.
Het boek begint met een uiteenzetting van het Cartesiaans dualisme en
voorbeelden van kritiek daarop. Vervolgens worden visies op de verhouding
tussen hersenen en geest besproken. Daarnaast is er een uiteenzetting over de
mogelijkheden van artificiële intelligentie. Tot slot wordt de vraag besproken
naar de aard van het bewustzijn en de menselijke identiteit.
literatuur

Maureen Eckert, red., Theories of Mind. An Introductory Reader. New
York, Rowman and Littlefield, 2006.
toetsing
schriftelijke tentamen.

subject
Intelligent Interactive Distributed Systems
code
400152
lecturers
prof.dr. F.M.T. Brazier; dr. T.B. Quillinan
credits
8
period
2 and 3
aim
The aim of this course is twofold. The first aim is to acquire knowledge and
insight in conceptual design of knowledge intensive systems in multi-agent
environments.
The second aim is to acquire skills in the following areas:
analysing and modelling complex domains and complex tasks in
general;analysing and modelling multi-agent environments in particular,
analysis of recent relevant literature; writing reports; presenting results.
content
In this course the main focus is on analysing, modelling and implementing
realistic, interactive, intelligent, distributed systems. A realistic example
domain involving trading agents is explored in the course and (mostly)
implemented.
form of tuition
A combination of lectures, meetings and practical work.
literature
To be announced during the course (notably articles).
mode of assessment
On the basis of exercises.
entry requirements
The courses Distributed Systems (400130) and Business Modelling and
Requirements Engineering (400010) are recommended for Computer Science
students. The courses Design of Multi-agent systems (400054) and
Behavioural Dynamics (400113) aanbevolen are recommended for Artificial
Intelligence students.
target audience
mCS, mAI
remarks
Attendance of lectures/meetings is obligatory; pre-registration is obligatory
for this course.
At the discretion of the lecturers, a selected number of students can
participate in the international Trading Agent Competition for an
additional 7 credits (code 400450). This competition takes place in the
months following the regular Intelligent Interactive Distributed Systems
course.

subject
Intelligent Web Applications
code
400153
lecturer
dr. R.M. Siebes
credits
8
period
1 and 2
Exam parts 25

aim
How to intelligently utilize huge, rich and shared web resources and
services taking into account heterogeneity of sources, user preferences and
mobility.
content
The World-Wide Web today is a huge network of information resources
which was built in order to broadcast information for human users.
Consequently, most of the information on the Web is designed to be suitable
for human consumption: The structuring principles are weak, many different
kinds of information co-exist, and most of the information is represented as
free text. With the increasing size of the web and the availability of new
technologies such as mobile applications or smart devices, there is a strong
need for making the information on the World Wide Web accessible to
computer programs which search, filter, convert, interpret, and summarize
the information for the benefit of the user.
The Semantic Web is a synonym for a World Wide Web whose accessibility
is similar to a deductive database where programs can perform well-defined
operations on well-defined data or even derive new information from
existing data.
This course addresses methods to create and use such a Semantic Web. It
extends and complements the "Web-based Knowledge Representation"
course by.
I. deepening the understanding of the formal foundations of knowledge
representation and reasoning on the web
A) Semantics of web languages
B) Reasoning in Semantic Web LanguagesII. investigating typical
application scenarios concerned with the use of distributed and
heterogeneous information on the web
C) Information Extraction
D) Information Integration
E) Information Access
form of tuition
Intensive lectures (in English), 2 times per week in the first 3 weeks. Per
week there is a short assignment about the topic discussed as preparation for
the large assignment where you make your own web application.
literature
Set of research papers.
entry requirements
Web-based Knowledge representation (required).
Knowledge Based Systems (preferred).
target audience
mAI

subject
Interactive Communication
code
470562
coördinator
drs. J.F.H. Kupper
lecturers
drs. J.F.H. Kupper; prof.dr. C.J. Hamelink; drs. B.J. Regeer
credits
3
period
13.10.2008-24.10.2008
aim

To acquire insight into the need for different ways of (professional)
communication

To understand the dilemmas and constraints, which have been identified
for interactive communication

To establish and put into practice a framework for analyzing interactive
communication

To practice skills in interactive communication
Artificial Intelligence (MSc)
26
content
Changes in society have resulted in a growing need for (more) interactive
communication. Within this course we analyze the change from Public
Relations as a one way stream (such as Postbus 51 commercials) to
interactive communication (such as debates, conversations) at three levels.
First of all, we assess the changes which have occurred within the societal
context which reduced the success of the one-way stream. What does the
transformation of the industrial society towards the network society mean for
communication strategies? And, what limitations are faced by interactive
communication at the macro-level (such as lock-in, resilience, institutional
tradition). Secondly, what does this mean for communication instruments?
For example, what is the difference between one-way and two-way
communication? How do you recognize the difference between a genuine
open dialogue and a debate between different points of view? Thirdly, what
are the constraints of interactive communication at the individual level? How
can you recognize these within conversations and debates? Assessment of the
relations and connections between the different levels forms an essential part
of the course. Students will gain insight into the relevant theoretical concepts
underlying the need for interactive communication.
form of tuition
Lecturers, self study, workshops, training workshops and individual
assignments.
literature
Reader
mode of assessment
Assessment is based on individual assignments, a group assignment and
active participation. All assignments need to be passed.
target audience
Optional course for Master students Management, Policy Analysis and
Entrepreneurship in health and life sciences (MPA), Science communication
and Societal differentiation of the Health, Life & Natural Sciences.
remarks
Attendance of workshops and training workshops is compulsory.
For information: frank.kupper@falw.vu.nl

naam
Interpersoonlijke communicatie
code
471007
coördinator
drs. I. Pauw
lecturers
drs. I. Pauw; D.T.A. Wols
studiepunten
3
aim
Development of:

insight in interaction processes/ how communication takes place in
groups;

skills for communicating in groups effectively, especially in management
roles.
content
This course is concerned with gaining insight in interaction patterns that take
place in a group. Your own contribution to the communication as a member
of a group and your possibilities to fulfill a "leader¿s role" are discussed.
We work with the Interpersonal Teacher¿s Behavior Model, which is used in
the secondary teacher training program but which is also applicable in other
situations. Effects of the `leader¿s` behavior on that of group members are
analyzed. Also, `effective¿ behavior will be trained.
form of tuition
Seminars and workshops during which theory will be analysed with the help
of video images and practice through active training; identifying interaction
patterns; training/rehearsing of communication skills.
literature
Reader
Exam parts 27

mode of assessment
On the basis of an assignment (e.g. via a video fragment), of which the
results will be displayed in the portfolio.
target audience
Optional course in the C-differentiations (Science Communication) of most
of the two year master programs of FALW and FEW.
period
29.09.2008-10.10.2008
remarks
Course is taught in Dutch. Maximum participants: 20

subject
Intracellular Networks
code
470622
co-ordinator
dr. K. Krab
lecturers
dr. F.J. Bruggeman; prof.dr. H.V. Westerhoff; dr. K. Krab;
prof.dr. J. Heringa; dr. J.L. Snoep
credits
6
period
27.10.2008-21.11.2008
aim
To train the students to analyse networks of cellular processes in terms of
systems properties (System Biology). Integration of knowledge about
individual processes and (spatial, temporal and organisational) structure of
networks in biological systems.
content
Enzyme kinetics; Metabolic and Hierarchical control Analysis; properties of
metabolic and signalling networks. Analysis of quantitative kinetic models of
such networks ('Silicon cells').
form of tuition
Lectures, self-study and computerpractical.
literature
Lecture notes (ca. 10 euro)
mode of assessment
Written exam and computer assignment.
target audience
Masterstudents with a background in Biology, Medical Biology,
Bioinformatics, Physics and Mathematics with an interest in the quantitative
analysis of the behaviour of biological systems.
remarks
Taught in English

subject
Introduction to Game Theory
code
400604
credits
6
target audience
mAI
remarks
This course is given at the UvA. For the description, please visit
http://studiegids.uva.nl/web/uva/sgs/nl/c/1994.html

subject
Knowledge Management and Modeling
code
400125
lecturers
dr. A.C.M. ten Teije; prof.dr. F.A.H. van Harmelen
credits
6
period
1 and 2
content
Knowledge management is a relatively new discipline which has as its aim
the efficiency improvement of the production factor "knowledge" and of the
related business processes (knowledge creation, distribution, application and
maintenance). The course "Knowledge Management and Modeling" is
concerned with the organizational aspects of knowledge management, as
well as the question how knowledge can be described with the support of
modern information-modeling techniques. These knowledge models can be
used to develop knowledge based systems. The notion of pattern-based
knowledge modeling is a key issue in the knowledge management process.
Artificial Intelligence (MSc)
28
Students carry out a knowledge-management project in small project groups
in a problem domain and organization of choice.
form of tuition
Lectures, assignments, group project.
literature
Schreiber, Akkermans, Anjewierden, de Hoog, Shadbolt, van de Velde,
Wielinga:
Knowledge Engineering & Management
. The MIT Press,
Cambridge MA, 2000, ISBN 0-262-19300-0.
mode of assessment
Assignment, project reports.
target audience
mIS, mAI

naam
Kwaliteitszorg van de informatievoorziening
code
400195
docent
B. Derksen (hoofddocent. e-mail: barry.derksen@itti.nl)
studiepunten
5
periode
1 en 2
inhoud
Het vak beoogt:

de student bewust te maken van een veranderende gebruikersattitude wat
betreft de informatievoorziening;

de student methodes te leren om structureel de kwaliteit van de
informatievoorziening te onderzoeken en te verbeteren;

de student bekent te maken met de praktijksituatie.

de student te voorzien van de huidige best practices op
informatievoorziening
Steeds meer professionele organisaties die zich bezighouden met de
informatievoorziening gaan over tot certificering van hun producten en hun
dienstverleningsproces. De kwaliteitsbeoordeling neemt een steeds
belangrijkere rol in voor de informatievoorziening. Evaluatie, assessment,
kwaliteitsbeheersing, kwaliteitsborging,integrale kwaliteitszorg en
verbetering van organisatie en processen zijn echter nog geen "levende"
termen. Dit college beoogt daarin verandering aan te brengen.
De leerstof wordt behandeld in 10 hoorcolleges waarin de volgende
onderwerpen aan de orde komen:

kwaliteit en kwaliteitszorg;

kwaliteitsinspectie, kwaliteitsbeheersing, kwaliteitsborging en integrale
kwaliteitszorg;

de infrastructuur van de informatievoorziening en de levenscyclus ervan;

de kwaliteit van de informatievoorziening;

kwaliteitszorg van de informatievoorziening;

verbeteren organisatie door kwaliteit van de informatievoorziening.
Het accent van het vak ligt bij het in de praktijk toepassen van de in de
colleges aangeleerde theorie, begrippen, principes en instrumenten.
werkwijze
De hoorcolleges worden gegeven aan de hand van verplichte literatuur
bestaand uit het boek: "
Modellen die werken, kwaliteit in bedrijf en
informatievoorziening
".
De in de hoorcolleges verkregen kennis dient te worden aangescherpt en
aangevuld door het bestuderen van de verplichte literatuur alsmede de
opgaven in de literatuur.
De opdrachtteams
De studenten dienen zich te organiseren in opdrachtteams. Het aantal te
vormen teams en het aantal studenten per team is afhankelijk van het aantal
inschrijvingen voor het vak en wordt tijdens een van de colleges meegedeeld.
Exam parts 29

De teams dienen de volgende opdrachten uit te voeren:

het uitwerken van een aantal cases die tijdens de colleges worden
aangereikt en het presenteren van de uitwerkingen

het uitvoeren van een opdracht na afloop van de hoorcolleges.
toetsing
De mondelinge toets

De kennis van en het inzicht in de tijdens de colleges gepresenteerde en in de
reader weergegeven stof, wordt tijdens een mondelinge toets geverifieerd. De
toetsen vinden plaats op nog nader vast te stellen tijdstippen.
De opdracht

Ten tijde van de colleges dient door ieder team een opdracht te worden
uitgevoerd. Dit kan een opdracht bij een externe organisatie (buiten de
Universiteit) zijn, of een interne opdracht binnen de Universiteit.
De docent maakt de opdrachten tijdens een van de colleges bekend. De teams
dienen uiterlijk tijdens het zesde college aan de docent bekend te maken
welke opdrachten zij kiezen. Hierbij geldt het principe "wie het eerst komt,
het eerst maalt".
Van iedere opdracht dient een verslag te worden gemaakt en een presentatie
te worden verzorgd.
De presentaties worden gegeven tijdens een "terugkomdag". In de mate van
het mogelijke zijn de opdrachtgevers van de externe opdrachten bij de
presentaties aanwezig.
Het verslag dient uiterlijk een werkweek voorafgaand aan de presentatie bij
de docent en, voor externe opdrachten, bij de externe opdrachtgever te zijn
ingeleverd.
Bij vragen, onduidelijkheden, problemen en dergelijke bij de uitvoering van
de opdracht dient de teamleider contact op te nemen met de docent.
Bij de eindpresentatie dient, waar mogelijk, gebruik te worden gemaakt van
de theorie, de termen, de begrippen en de instrumenten uit de hoorcolleges en
de verplichte literatuur. De eindpresentatie dient goed gedocumenteerd en op
zichzelf leesbaar te zijn. De eindpresentatie mag niet langer dan dertig
minuten duren (na verloop van dertig minuten zal de docent de presentatie
afbreken).
De voor het verslag en de presentatie te maken kosten zijn voor rekening van
de studenten.
doelgroep
3IMM, mIS
opmerkingen
Diverse gastsprekers uit het bedrijfsleven worden uitgenodigd.

subject
Literature Study
code
400277
lecturer
various lecturers (Students should consult their mentor to find a topic and a
supervisor.)
credits
6
period
Variable
aim
Students will learn to:

conduct autonomously a literature study;

search and select bibliographic material that is relevant for the chosen
topic;

give a presentation where they explain the research problem and present
the state of the art.
content
The course consists of carrying out a literature study on a topic chosen in
Artificial Intelligence (MSc)
30
agreement with a supervisor. Students select a topic of their interest that they
particularly like, contact a person involved in the relevant research area and
discuss with him/her the possibility to carry out a literature study under
his/her supervision. Once agreed on the topic the study is carried out in two
phases:

Students autonomously search, select and study relevant related
bibliographic material (i.e. papers, reports, books etc.)

Give a written and/or oral presentation of the topic covered for an
audience of computer scientists, for instance by giving a slide show.
The exact form of presentation should be discussed and agreed upon with the
supervisor. A clear indication of the used sources is an essential element of
the presentation.
form of tuition
Supervision by a faculty member.
mode of assessment
Written and/or oral presentation (in English), exact form to be agreed with
the supervisor.
target audience
mCS, mAI-TAI
remarks
http://www.few.vu.nl/~kmitrok/literature_study2008.html

subject
Logical Verification
code
400115
lecturer
dr. F. van Raamsdonk
credits
6
period
1 and 2
aim
Introduction to type theory and the proof-assistant Coq.
content
A proof-assistant is used to check the correctness of a specification of a
program or the proof of a theorem. The course is concerned with the proof-
assistant Coq which is based on typed lambda calculus. In the practical work,
we learn to use Coq. One of the exercises is concerned with the correctness
proof of the specification of a sorting algorithm, from which a functional
program is extracted. In the course, we focus on the Curry-Howard-De
Bruijn isomphism between proofs on the one hand and lambda-terms (which
can be seen as functional programs) on the other hand. This is the basis of
proof-assistants like Coq. We study various typed lambda calculi and the
corresponding logics.
form of tuition
This is a 13-weeks cours with 4 hours class every week:
2 hours theory and 2 hours practical work.
literature
Course notes.
mode of assessment
A written examination plus exercises. It is imperative to have a
sufficient mark for the exercises.
entry requirements
Inleiding logica (400119).
target audience
mCS, mAI, mMath

subject
Machine Learning
code
400154
lecturer
drs. E.W. Haasdijk
credits
6
period
2
aim
The course Machine Learning (ML) surveys methods of acquiring and/or
modifying theories from observations.
content
Learning is one of the fundamental attributes of intelligence, and ML is
Exam parts 31

currently the most active area of research in AI. The main topics covered in
the course are:

concept learning and the general-to-specific ordering

decision tree learning;

artificial neural networks;

evaluating hypotheses;

bayesian learning;

instance-based learning;

Genetic Algorithms;

learning sets of rules;

reinforcement learning.
form of tuition
Lectures with final written examination.
literature
Tom Mitchell,
Machine Learning
. Mc Graw Hill, 1997 ISBN 0-07-042807-7.
mode of assessment
Written eximination.
target audience
3BWI, 2AI, mCS
remarks
Students are required to sign up for this course at Blackboard and via TIS:
https://tis.vu.nl/tis/menu

naam
Management en Organisatie 1.1
code
60111030
studiepunten
3
contacturen
18 (6 activerende werkvormen, 12 hoorcollege)
periode
1
coördinator
drs. G.P. Melker
docenten
drs. G.P. Melker; drs. M.J. Visser
doel

Het ontwikkelen van je theoretische kennis op het gebied van strategisch
management, besluitvorming en maatschappelijk verantwoord
ondernemen;

Het ontwikkelen van vaardigheden om relevante informatie te
verzamelen en te analyseren met betrekking tot actuele vakrelevante
cases;

Het ontwikkelen van je schriftelijke rapportagevaardigheden en je
mondelinge presentatievaardigheden
inhoud
In dit vak staat het interne en externe functioneren van een bedrijf in de
markt en de maatschappij centraal. De organisatie wordt gezien als een
samenwerkingsverband van belanghebbenden gericht op het realiseren van
specifieke organisatiedoelen (marktpositie, winst, maatschappelijke
verantwoordelijkheid et cetera). Binnen het vak wordt ingegaan op
strategisch management, strategieformulering, besluitvorming en
denkrichtingen binnen het vakgebied van management en organisatie.
Tijdens de activerende werkvormen word je in de gelegenheid gesteld om de
aangereikte theorie toe te passen met behulp van actuele cases. Dit kunnen
recente krantenartikelen zijn of andersoortige voorbeelden uit de praktijk. De
activerende werkvormen hebben dan ook een hoog praktijkgehalte.
werkwijze
Hoorcolleges en activerende werkvormen. Tijdens de hoorcolleges worden
de hoofdlijnen van de verplichte literatuur behandeld. Bij de activerende
werkvormen staan de toepassing van de theorie en de voorbereiding op het
tentamen centraal.
literatuur

Keuning, D. & D.J. Eppink, Management & Organisatie. Theorie en
Toepassing. 9e druk. Groningen/Houten: Wolters-Noordhoff, 2008.
Artificial Intelligence (MSc)
32

Keuning, D. & D.J. Eppink, Werkboek Management & Organisatie. 9e
druk.Groningen/Houten: Wolters-Noordhoff, 2008.

Keuning, D., (2008). Management & Organisatie. 33 Cases. 9e
druk.Groningen/Houten: Wolters-Noordhoff, 2008.

De overige literatuur wordt via de studiewijzer van periode 1.1 bekend
gemaakt.
toetsing
schriftelijk tentamen
Multiple choice vragen over de verplichte literatuur en de stof die tijdens de
hoorcolleges is behandeld; kennis- en toepassingsgerichte multiple choice
vragen.
entreevoorwaarden
Geen. De inhoud van het vak Management & Organisatie 1.1, 2.1 en 2.5
wijkt sterk af van de inhoud van het vak Management & Organisatie dat op
het vwo wordt gedoceerd. Voorkennis van het vwo-vak Management &
Organisatie is dan ook niet noodzakelijk.
opmerkingen
Tijdens de activerende werkvorm zullen de studenten werken aan een
praktijkcase waarmee de studenten reeds een indruk krijgen op welke wijze
de aangereikte leerstof kan worden toepast binnen organisaties. Ook in de
perioden 2.1 en 2.5 zal het vak Management & Organisatie worden verzorgd.
De complexiteit van de cases neemt naarmate de perioden vorderen steeds
verder toe. Uiteindelijk ben je in staat om de verschillende inzichten en
concepten op het gebied van management en organisatie te integreren en om
de aangereikte theorie te vertalen in praktisch toepasbare oplossingen. Bij het
oplossen van cases zal gebruik worden gemaakt van de 'casemethodiek-in-
vijf-stappen'.

subject
Master Project AI for the Communication Variant
code
400538
lecturer
various lecturers
credits
21
target audience
mAI (specialization AI and Communication)
remarks
This is the Master Project for the specialization AI and Communication. In
this specialization there are two major projects, one in the Communication
part and the other (this one) in the AI part.
Therefore the number of credit points is only 21 for each project.

subject
Master Project Artificial Intelligence
code
400285
credits
30
period
4, 5 and 6
period
Variable.
aim
The Master programme in Artificial Intelligence is a scientific programme
that aims to provide the student with the knowledge, experience and insights
needed to autonomously carry out his/her professional duties. The
programme is designed to prepare the student for further education as
scientific researcher (Ph.D. studies) as well as to offer a solid basis for a
career in business at an academic level. Moreover, the programme aims at
educating the student as to acquire a practical understanding of the position
of the field of Artificial Intelligence within a broad scientific, philosophic
and social context.
content
Each Master AI programme is finished with a master project AI . This can
Exam parts 33

be an individual project as well as a group project. Information about
projects (incl. internships) can be found on the Internet pages of the AI
divisions. Internships proposed by the student him/herself need approval in
advance from a member of staff, who will also be involved with supervising
the project.
The size of the graduation projects is as such that with adequate
foreknowledge and complete study, the project can be finished within 6
months.
The student participates in the KIM (Kunstmatige Intelligentie Meeting).
See blackboard KIM.
form of tuition
The Master Project has always to be supervised by a staff member, in the
case of an internship in cooperation with a supervisor in the company.
Internships proposed by the student him/herself need approval in advance
from a member of staff, who will cooperate with supervising the project.
mode of assessment
The final grade will be based on the quality of the research, the written
thesis, the KIM presentations and the participation in the KIM.
target audience
master AI (variants: KTIIA, CISO, TAI, Interdisplinary)
remarks
For all rules, assessment criteria, contact persons, and many practical tips for
your master project, see the KIM blackboard page (inclusive the "Manual for
the Master Project AI").

subject
Master Thesis: Research Project Cognitive Science
code
815067
credits
30
period
4, 5 and 6
aim
To learn how to perform research and report about it. Projects involve basic
research, applied research, research concerning modeling, or a combination
of these.
content
Students participate in a research project concerning Cognitive Science. The
Thesis can be done at the department of Cognitive Psychology (FPP), the
department of Artificial Intelligence (FEW), an external research
organization (for example TNO), a company, or another (foreign) university.
Before starting, a written research plan should be submitted to the head of the
department of Cognitive Psychology or the head of the department of
Artificial Intelligence. Participation in a research project can only start after
approval of the research plan. The research performed by the student forms
the basis for the Thesis. The Master Thesis should be written in article style.
Students will be supervised by a person from the academic staff of the
department of Cognitive Psychology or the department of Artificial
Intelligence. There will be at least one meeting a week between the student
and the supervisor.
mode of assessment
The final grade for the Master Thesis will be based on the quality of both the
research and the written thesis. Grading will be done by the direct supervisor
and the head of the department.
It is required that students present their research in the form of a talk during a
research meeting. Students are also required to attend at least four research
meetings at the department of Cognitive Psychology. It is finally required
that students participate in the KIM meetings according to the rules as
outlined on the web-site of the KIM meetings.

Artificial Intelligence (MSc)
34
subject
Memory and Memory Disorders
code
815102
credits
6
lecturer
dr. R.J. Godijn
period
2 (in 08/09; not in 09/10)
aim
The course aims to give students an overview of memory at the cognitive and
neurophysiological level, and to give students the background to interpret
memory disorders in patients with brain damage.
content
The course focuses on various approaches in the study of human memory
and memory disorders. We will discuss working memory, encoding-retrieval
interactions, interference and forgetting implicit memory, and the brain
substrate of memory. We will also discuss clinical testing of memory, and
memory loss after local brain damage, dementia, and other conditions. We
will review a number of theoretical frameworks such as REM, neural
networks, and ACT-R.
form of tuition
12 two-hour lectures and workshops, two oral presentations and a paper
literature
To be announced
mode of assessment
Exam, presentation, and term paper

subject
Mini Master Project AI
code
400428
lecturer
dr. M. Hoogendoorn
credits
6
period
Throughout the year
aim
Gaining deeper insight into a specific topic in AI.
content
This course consists of a small project on a specific topic in AI, selected in
agreement with your supervisor. The project may have various forms, such as
a literature study, the design of a piece of software, or exploring a research
question. The results of the project are described in a brief report.
form of tuition
Individual project and written report.
mode of assessment
The end grade is based on both the project and the written report.
target audience
mAI
remarks
Depending on the interest of the student, a specific topic is selected and an
individual supervisor is assigned.

subject
Multimedia Authoring
code
400440
docent
dr. A. Eliens
credits
6
period
1
aim
The course gives a practical introduction to multimedia authoring, in
particular the development of 3D web applications.
content
In the course an extensive introduction to the use of VRML (Virtual Reality
Modeling Language) is given. Topics treated include the construction of 3D
objects, positioning of objects in 3D space, material, light and animation.
Also the use of images, video and sound to augment the users experience will
be treated. Ample attention will be given to the programmatic interface to
VRML, including prototypes and scripting, needed for the development of
interactive applications.
The assignments include a 3D product demo and an infotainment application.
Exam parts 35

form of tuition
lectures and practicum.
literature
Online syllabus.
mode of assessment
Practicum assignments.
target audience
2IK-minor MMC, mCS-MM and interested students.
remarks
For course information, see
www.cs.vu.nl/~eliens/mma

For the course material, see
www.cs.vu.nl/~eliens/web3d


subject
Museologie en buitenschoolse educatie
code
471026
co-ordinator
drs. R.C. van Koten MSc
lecturers
drs. R.C. van Koten MSc; guest lecturers
credits
6
period
24.11.2008-19.12.2008
aim

Gain insight in the role of museum exhibits in the field of science
communication

Apply theoretical notions of science communication and science
education to perform science communication research in museum
settings

Apply qualitative and quantitative research methods to
design/perform/report on research project in museum settings

Learning to advise on adjustments of extracurricular (teaching) materials
and museum exhibits
content
This course consists of lectures on the role of science museums/centers, zoos
and natural history museums in science communication. You will get
familiar with theories of science communication and informal science
education in museum setting, introducing different educational methods as
well as styles of communication, and different methods of research and
evaluation of exhibitions.
Guest speakers give insight into their profession as science communicators in
museums and science centers, as researchers in the field of museology and as
professionals in developing informal science learning programs. Excursions
are an important part of this course as an introduction to the actual working
field. Through several assignments you are encouraged to combine theory
and practice. The assignments are developed in collaboration with four
institutions for informal (science) learning, such as NEMO, Naturalis and
Artis.
form of tuition
Lectures, seminars, excursions, assignments and home-study
literature
Reader, provided at start of course
mode of assessment
Assignments (40%), presentation (10%), exam (50%)
For all assignment, presentation and exam a pass-grade must be obtained
entry requirements
Bachelor in any of the Beta Sciences
target audience
Optional course in the C-differentiations (Science Communication) of most
of the two year master programs of FALW and FEW
remarks
Course is taught in Dutch (with the possible exception of foreign guest
speakers).
For information: reinout.van.koten@falw.vu.nl




Artificial Intelligence (MSc)
36
subject
Network Programming
code
400052
credits
9
period
4 and 5
lecturer
dr.ir. G.E.O. Pierre
aim
Let the student get familiar with the development of network applications.
content
The course discusses a number of programming facilities for the
development of network applications. Attention is paid to designing and
implementing applications with threads, sockets, RMI/RPC, CGI/BIN,
servlets, PHP. In addition, attention is paid to security and modern enabling
technologies like peer-to-peer systems.
form of tuition
Lectures combined with lab assignments.
mode of assessment
Lab assignments plus an exam.
entry requirements

Introduction to Computersystems (400033);

knowledge of C

preferred: Computer Networks, Distributed Systems.
target audience
mCS, mPDCS
remarks
Registration for this course is compulsory via the class Web site,
http://www.cs.vu.nl/~gpierre/courses/np/
, two weeks prior to the start.

subject
Neural Models of Cognitive Processes
code
815051
credits
6
lecturer
dr. M. Meeter
period
2 (in 09/10; not in 08/09)
aim
Neural network models have become part of the fabric of cognitive science,
and have been applied in many domains. In this course, we will concentrate
on these applications, and on hands-on experience with the development of
neural network models.
content
The course will start with a general introduction, and a tutorial in a
simulation environment. In the second part of the course, students will
present published models, and be required to either extend a model or to do
several exercises in the simulation environment. This work then has to be
described in short papers.
form of tuition
22 hours lectures and discussion, 4 hours computer tutorial, one oral
presentation, 30 hours group work, 10 hours activating work form.
literature
Syllabus, and a reader with recent papers.
mode of assessment
Grades are based on average of performance on a final exam, the oral
presentation and the term paper.

subject
Neural Networks
code
400132
lecturer
dr. W.J. Kowalczyk
credits
6
period
1
aim
Introduce the student to the most popular neural network models and their
applications.
content
The course provides an introduction to the basic neural networks
architectures and learning algorithms. The following main topics are
covered: single layer perceptrons, LMS algorithm, multilayer perceptrons,
Exam parts 37

radial-basis function networks, support vector machines, self-organizing
maps, discrete Hopfield model, brainstate- in-a-box model. Moreover, typical
applications of neural networks are discussed.
form of tuition
Oral lectures and compulsory programming assignments.
literature
To be announced later.
mode of assessment
Assignments and written examination.
target audience
3AI, 3I, 3BWI, mCS, mBMI
remarks

Lectures in English.

Course registration is compulsory and must be done on the first day of
lecture directly with the lecturer.

subject
Ontology Engineering
code
400292
lecturer
prof.dr. A.T. Schreiber
credits
3
period
6
content
Ontologies are nowadays used in computer science a means to share common
concepts between information systems, This course is focused on theory,
methods, and tools for constructing and/or extending ontologies for this
purpose. Teaching subjects typically center around engineering principles,
e.g. for subtype hierarchies (backbone identification, viewpoints, dimensions,
constraint specification), part-of structures (types of part-of relations,
representation of part-of relations), and default knowledge. Also, the
mapping and/or integration of different ontologies is discussed. The course
contains examples of how ontologies are used in practice. The assignments
focus on real-life examples of ontologies currently in use in web
applications.
form of tuition
Lectures, assignments.
literature
Reader.
mode of assessment
Assignments, self evaluation.
entry requirements
Web-gebaseerde kennisrepresentatie (400083).
target audience
mIS

subject
Perception
code
815047
credits
6
period
5
lecturer
dr. C.N.L. Olivers
content
Introduction to the fundamental principles of perception. Physiological,
psychophysical and cognitive approaches to visual, auditory and tactile
perception are treated. Is perception purely a registration of the outside
world? Which processes and representations underlie conscious and
unconscious perception? What methods can we use to find out?
form of tuition
Lectures, literature study
literature
Goldstein, E.B. (2006) Sensation and Perception. 7th Edition. London:
Wadsworth. As well as a selection of articles (to be announced in class).
mode of assessment
Written exam and in-class assignments.
entry requirements
No specific requirements


Artificial Intelligence (MSc)
38
naam
Project Software Engineering
code
400067
docent
dr. P. Lago
studiepunten
8
periode
5 en 6
inhoud
Het doel van het SE project is de theorie opgedaan in het SE college toe te
passen in een zo'n realistisch mogelijke praktijksituatie. Het project bestaat
uit het construeren van een groot programma in teamverband volgens de
RAD (Rapid Application Development) methode. Zoveel mogelijk aspecten
van projectmatig werken en software engineering zullen hierbij aan de orde
komen, waaronder het opstellen van een projectplan, requirements
engineering, design, implementatie en testen, maar ook het samenwerken in
een team.
werkwijze
Het uitvoeren van een project in teamverband (4 à 5 personen) met 'progress
report' presentaties van ongeveer 15 minuten.
literatuur
Vliet, H. van,
Software Engineering, Principles and Practice
, John Wiley.
Third edition (2008).
Martin Fowler,
UML Distilled 3rd edition
. Addison Wesley, 2003.
toetsing
Een team wordt beoordeeld op samenwerking (10%), kwaliteit van de
documentatie (25%), kwaliteit van de opgeleverde producten (25%), de
consistentie van de documentatie en het eindproduct (10%),
projectpresentatie (10%) en een individuele evaluatie (20%).
doelgroep
2I, 2IMM, 2BWI, 3AI, 3IMM-BI, 3IMM-MMC
voorkennis
Vereiste voorkennis: Software engineering (400071)
opmerkingen
Inschrijven voor dit vak is verplicht via Blackboard en via TIS
https://tisvu.vu.nl/tis/menu
tot 2 weken voor aanvang.
Voor meer informatie zie Blackboard:
http://bb.vu.nl
.

subject
Protocol Validation
code
400117
lecturer
prof.dr. W.J. Fokkink
credits
6
period
5 and 6
aim
Learning to use formal techniques for specification and validation of
communication protocols.
content
This course is concerned with specification and validation of protocols, using
formal methods. The course is based on a specification language based on
process algebra combined with abstract data types, called mCRL. This
language and its toolset can be used for specification of parallel,
communicating processes with data. Model checking is a method for
expressing properties of concurrent finite-state systems, which can be
checked automatically. Interesting properties of a specification are:
"something bad will never happen" (safety), and "something good will
eventually happen" (liveness). In the lab we will teach the use of a tool for
automated verification of the required properties of a specification.
form of tuition
Lectures with practical work. During the labs the mCRL-tool and a model
checker will be used for validation of protocols discussed during lectures.
literature
Wan Fokkink,
Modelling Distributed Systems
, Springer 2007.
mode of assessment
Written exam, together with a homework assignment. The overall mark of
the course is (H+2W)/3, where H is the mark for the homework assignment,
Exam parts 39

and W is the mark for the written exam.
target audience
mCS, mPDCS
recommended
background knowledge
Datastructuren
remarks
Once every other year, not in spring 2009.

naam
Qualitative and Quantitative Research Methods
code
470582
coördinator
drs. F. Kupper
lecturers
drs. R.C. van Koten MSc; drs. F. Kupper; M.G.B.C. Bertens; guest lecturers
studiepunten
6
aim

Understanding the difference between beta- and gamma research

Hypothesis development on how to bring scientific knowledge to the
public (understand science so to help society) and how to bring insights
of the public back to science (understand society to help direct scientific
questions)

To acquire further insights into various quantitative and qualitative
research methods of data collection and analysis, such as interviews
(structured, semi-structured and open), focus groups, surveys
(postal/internet), structured questionnaires, participative research and
experimental design

Know how to interpret quantitative and qualitative findings

Familiarity with univariate and multivariate analysis techniques as well
as data mining and neural net analysis

To make an adequate research design for the investigation of a specific
societal or communication problem with regard to science and a specific
science problem with regard to communication and society
content
Contemporary societies increasingly face complex social problems related to
science and technology, like climate change, HIV/ AIDS or the introduction
of nanotechnology. Those complex social problems, for example the loss of
b
iodiversity or the containment of infectious diseases, manifest themselves at
different levels of society. By definition, they involve a variety of social
actors: policy-makers, professionals, NGOs, industry, science and of course
the public at large. Addressing these complex issues therefore demand for an
interdisciplinary approach. This course offers an advanced introduction to
various quantitative and qualitative research methods used in
interdisciplinary research. You will acquire knowledge and skills to operate
at the interface of your natural science discipline and society, thereby making
a contribution to answering the complex social problems in these areas. You
will acquire further insight and understanding of different quantitative
methods, including surveys and structured questionnaires and qualitative
research methods, including interviews (open semi-structured) and
participatory methods such as focus group discussions. In addition, you
deepen your knowledge on the design of interdisciplinary research to collect,
analyse, and integrate information of a variety of actors that are involved in a
societal dilemma.
In the fourth week of this course you will apply the theoretical knowledge
gained in the previous three weeks by designing your own study, which
should include a selection of research methods.
form of tuition
Lectures, training workshops, self study
Artificial Intelligence (MSc)
40
literature
Reader or Book (Details will be announced on blackboard)
mode of assessment
Based on a written exam, an individual assignment and active participation.
All assignments need to be passed.
target audience
Compulsory course in the Masterprogramme Management, Policy Analysis
and entrepreneurship for the health and life sciences (MPA) and compulsory
course within the Science communication- and Societal differentiations of
Health, Life and Natural Sciences Masters programmes.
period
01.09.2008-26.09.2008
remarks
Attendance of training workshops is compulsory.
For further information please contact frank.kupper@falw.vu.nl

subject
Qualitative Research Methods for the Information Sciences
code
400290
lecturer
prof.dr. J.M. Akkermans
credits
3
period
3
aim
This course helps prepare students who want to embark on their (Master)
research.
content
The course provides an overview and assessment of different scientific
research methods, needed in a multi-disciplinary approach to Information
Systems and how they function in an organizational context. Topics are:

developing the research questions you want to answer;

make a research design and planning your research;

research methods relevant for IS (e.g. interview, case study, action
research, ethnography, survey, modelling, simulation, prototyping);

aspects of theory formation and validation; triangulation

how do you (and others) know that your research results are valid?;

research report writing.
form of tuition
Workshop-like. In two consecutive (full) days we will not just discuss
textbook material, but do several hands-on exercises and assignments in
class. Furthermore, a critical review of existing IS Master theses has to be
written.
literature

Reader with recent articles

Pervez Ghauri and Kjell Gronhaug,
Research Methods in Business
Studies
3rd ed. Prentice Hall, Essex, UK, 2005.
mode of assessment
Written review essay, active workshop participation, and written
examination.
entry requirements
Bachelor-level IMM, I or AI
target audience
mIS, mCS, mAI
remarks
A useful reference point is the protocol that specifies the procedures and
criteria for Master research in IS (see study guide).

subject
Review Paper
code
815104
credits
6
period
3
aim
To familiarize students with the literature concerning the topic of research of
their Master Thesis Cognitive Neuropsychology. In addition, it is aimed that
students learn to write a review paper under close supervision.
content
Depends on the topic of research during the Master Thesis
Exam parts 41

form of tuition
Students will be individually monitored and instructed by their supervisor in
writing a literature review.
literature
Depends on the topic of research during the Master Thesis
mode of assessment
Paper

naam
Science and Communication
code
470587
co-ordinator
prof.dr. C.J. Hamelink
lecturers
prof.dr. C.J. Hamelink; dr. J.E.W. Broerse; dr. K.T. Rebel; dr. I.R. Hellsten;
drs. B.J. Regeer; guest lecturers
studiepunten
6
aim

To put practical knowledge of science communication (e.g. journalism,
museology) in the theoretical context of science communication research;

To gain theoretical insight in the dynamic relationship between science
and society;

To deepen knowledge of different models for science communication;

To acquire in-depth knowledge about how to assess the effectiveness of
interactive policy processes; and

To learn about the most recent developments in science communication
and in communication sciences in general.
content
In the context of the changing dynamics within and between science and
society, it becomes increasingly important to understand the types of
communication processes at the core of several interfaces; communication
between scientists from different disciplines, between different sciences and
their stakeholders, and between science and the public. This module starts
with a reflection on science and knowledge from different perspectives:
Questions that will be addressed include: What is science? What does it
mean to develop scientific knowledge? and How does the development of
that knowledge relate to other social and cultural processes? With this
reflection in mind, the course will cover the current state-of-the-art in science
communication research (e.g. models of science communication) and in
communication science in general, which will be applied to real-life
examples from science journalism, new media and museum exhibitions. In
addition, top scientists from different scientific disciplines will give lectures
about their views on and experiences with science communication.
form of tuition
Lectures and seminars on theory and practice of science communication.
literature
Book "The Golem: What you should know about science" and articles posted
on blackboard.
mode of assessment
Assessment based on an individual essay assignment and group assignment.
Both assignments need to be passed.
target audience
Compulsory course for Master students in the C-specialisation (Science
Communication) of the Masters Biomedical Sciences, Biology and any of the
natural sciences
Optional course for Master students Management, Policy Analysis and
Entrepreneurship in health and life sciences (MPA), M-specialisation of the
Masters Biomedical Sciences, Biology, and any of the natural sciences
period
05.01.2009-30.01.2009
remarks
Students in health, life and natural sciences who are not enrolled in the C-
specialisation have preferably taken one or more courses in (practical aspects
of) science communication.
Artificial Intelligence (MSc)
42
For information and application: karin.rebel@falw.vu.nl

naam
Scientific Writing in English
code
400592
docent
drs J.K.A. Meijer
studiepunten
3
aim
The aim of this course is to provide the writing student with the essential
linguistic means for producing English academic texts which are effective,
idiomatically and stylistically appropriate and grammatically correct.
content
The initial focus in the course lies on the form of scientific texts in the Exact
science. You will be making considerable use of peer assessment: examining
fellow students' written work and giving them feedback. This method
provides useful insights into how a text might be improved. The process of
providing someone else with feedback on their text is something that you
will find very instructive.
form of tuition
The course is focussed on self-tuition. The plenary sessions concentrate on
the process of writing and the product of writing. Homework is part of the
course. With each topic, participants work through a phased series of
exercises that usually conclude with the requirement to write a short piece of
text. The instructor will append extensive written remarks to this text.
literature
The reader `Writing a Scientific Article' can be obtained at the Taalcentrum-
VU in the Metropolitan (4th floor) . The costs are 15 euro.
mode of assessment
There will be no examination. However, students will receive their credits
only when they have participated in the classes and also when they have
handed in all of the assignments. Students will receive a 'pass' when they
have finished the course.
entry requirements
Bachelor Exact Sciences
target audience
mAI, mBMI, mCh, mDDS, mMath, mMNS, mPhys
period
Various dates around the year, see timetable masters
remarks
Taught in English

subject
Seminar Attention (Seminar Attention)
code
815100
credits
6
period
5 and 6
lecturer
prof.dr. J.L. Theeuwes
aim
To learn how to interpret and analyze theories and findings on attention and
eye-movements. Learn how to set up experiments.
content
The format of the seminar will be a discussion of one or two target articles,
and student presentations, each week. Target articles for each week will be
"classic" articles representing early and/or important studies on a specific
topic or recent new papers in attention and eye movements. For the
presentations, each student has to present the main findings of the target
article for that week and is required to find a recent paper on the topic
covered by the target article. Students have to prepare a 20 minute oral
presentation in Microsoft Powerpoint. The rest of the class will be spent
discussing the target articles and their relationship to the presented papers.
Each student will give two presentations. The presentation will determine
50% of the course grade for each student. The target papers will be available
on the course website and accessible via blackboard.
Exam parts 43

One week after the last class, each student will submit a final paper (up to 20
pages, 12 pt. font, double spaced) on one of the topics covered in class. The
paper will consist of a brief review of (at least) 6 research papers (including
those already covered on that topic in class) and a proposal for a new
experiment. The paper will be worth 50%.
form of tuition
Lectures and practical assignments
literature
Articles
mode of assessment
Student presentation and writing a paper. Students are required to be present
during all meetings. Penalty for being absent is 5% each time a student is
absent.
remarks
The course Attention (Dr. W. van Zoest; BA3) is required to enroll.

subject
Seminar Cognitive Neuroscience
code
815098
credits
6
period
4
lecturers
dr. D.J. Heslenfeld; dr. C.N.L. Olivers
aim
To extend students' knowledge in the field of cognitive and
clinical neuroscience.
content
Over the last two decennia, scientific research in the field of cognitive
neuroscience has led to fundamental new insights in the relation between
brain function and behavior. Research is ongoing, and in many cases, the
latest insights have not yet traversed their ways down into the regular
textbooks. This seminar offers students the possibility to discuss state of the
art research. The latest insights into topics such as working memory,
multisensory perception, and the mirror neuron system will be covered. The
seminar will also cover important questions regarding legal and ethical
aspects of cognitive and clinical neuroscience research.
form of tuition
Lectures, literature study, oral presentations and discussions.
literature
Research papers; to be announced.
mode of assessment
Oral presentation, contribution to discussion, and a review paper.
entry requirements
Cognitive Neuroscience and Neuropsychology

subject
Software Architecture
code
400170
lecturer
prof.dr. J.C. van Vliet
credits
6
period
2 and 3
aim
Get acquainted with the field of software and information architecture.
Understand the drivers behind architectural decisions. Be able to develop and
reason about an architecture of a non-trivial system.
content
Students work in groups to develop an architecture for a fictitious system.
They have to develop different representations (called views) of the
architecture. These different representations emphasize different concerns of
people that have a stake in the system. Each group will also be asked to
assess ("test") the architecture of another group for certain quality attributes.
form of tuition
Group work with a number of assignments
literature
Len Bass et al,
Software Architecture in Practice
second edition. Addison-
Wesley, 2003.
mode of assessment
Written reports of the assignments, presentation, exam.
Artificial Intelligence (MSc)
44
entry requirements
Software Engineering.
target audience
mCS, mIS
remarks
Students are required to sign up for this course at Blackboard and via TIS
(
https://tisvu.v.unl/tis/menu
) at least 2 weeks before the course starts.
For details, see the Blackboard system
http://bb.vu.nl
.

subject
Special Topics Cognitive Science
code
400560
docent
dr. T. Bosse
credits
9
period
1, 2, 3, 4, 5 and 6
content
The aim of this course is to eliminate specific deficiencies in the areas of
Artificial Intelligence and Cognitive Psychology. Each student will take part
in an individually developed course consisting of a range of topics covering
the basics of Artificial Intelligence, Cognitive Psychology, or both,
depending on the specific deficiencies present.
In order to determine the individual content of the course program, students
are required to make an appointment with the course coordinator. The
individually tailored course program will contain (a subset of) the following
elements: principles of programming, propositional and predicate logic,
knowledge-based systems, multi-agent systems, cognitive neuroscience and
neuropsychology, and principles of (experimental) research design. Although
most of these elements address basic principles of Artificial Intelligence or
Cognitive Psychology, the pace and the difficulty of the program will be at
Master level.
form of tuition
Lectures, self study, practical work
literature
Dependent on individual
mode of assessment
Individual assignments
target audience
mAI (specialization Cognitive Science)

subject
Statistical Data Analysis
code
400073
lecturer
prof.dr. M.C.M. de Gunst
credits
6
period
1, 2 and 3
aim
The course introduces the students to several widely used statistical models
and methods, and the students are taught how to apply these tools to real data
while using the statistical software package R.
content
The following subjects are covered:

introduction to the statistical package R;

summarizing data;

investigating the distribution of data;

Q-Q plots;

robust methods;

non-parametric methods;

bootstrap;

two-sample problems;

contingency tables;

regression analysis.
form of tuition
Lectures, exercises with computer, discussion of exercises.
Exam parts 45

literature
Lecture notes and R manual.
mode of assessment
Via weekly homework assignments and extended final assignment.
entry requirements
Algemene Statistiek (400004) or Algemene Statistiek voor BWI (400218)
target audience
3W, mMath, 3BWI
remarks
Please note: Admission is limited; enrollment via TIS,
https://tis.vu.nl/tis/menu
, is compulsory.
The statistical package R can be downloaded for free from:
http://www.r-project.org/
.

subject
Thinking and Deciding (Denken en Beslissen)
code
815049
credits
6
period
2
lecturer
dr. M.R. Nieuwenstein
aim
Explaining and providing understanding of theories, research methods and
practical aspects about human judgment, rational thinking, dilemmas, choices
and planning.
content
What is "rational" thinking? What keeps us from it? How can we improve
our thinking and decision processes? How do we reason and choose in
uncertain (risk) situations? What is the influence of (moral) beliefs and
emotions?
form of tuition
Lectures
literature
Baron, J. (2000) Thinking and Deciding (3rd ed.). New York: Cambridge
University Press.
mode of assessment
Written exam

naam
Voortgezette logica
code
400410
docent
dr. R.D.A. Hendriks
studiepunten
4
periode
4
doel
De modale logica werd al kort geintroduceerd in het vak Inleiding Logica.
Doel van dit college is de verdere verdieping van inzicht en vaardigheden in
de modale logica, met het oog op toepassingen in Informatica en
Kunstmatige Intelligentie.
inhoud
De modale logica bestaat in verschillende gedaantes, bijvoorbeeld
tijdslogica, kennislogica, dynamische logica, deontische logica, en al deze
vormen hebben hun eigen specifieke toepassingen. Maar het theoretisch
kader is steeds hetzelfde: Kripke-modellen met mogelijke werelden en
toegankelijksheidsrelaties. Bij een specifieke vorm van modale logica,
bijvoorbeeld kennislogica, horen dan wel specifieke eigenschappen van de
toegankelijkheidsrelaties. Een belangrijk technisch hulpmiddel bij de
bestudering van een modale logica is bisimulatie tussen Kripke-modellen. In
de dynamische logica slaan de modaliteiten op het gedrag van programma's
in een programmeertaal voor het samenstellen van atomaire acties.
werkwijze
2 uur per week hoorcollege en 2 uur per week werkcollege.
literatuur
Collegedictaat.
toetsing
Schriftelijk tentamen (plus facultatief twee collecties inleveropgaven
waarmee 0,5 bonus punt kan worden verdiend).
doelgroep
3I, 3AI, mAI, mCS (ook geschikt als keuzevak Wiskunde)
Artificial Intelligence (MSc)
46
voorkennis
Inleiding Logica (400119)

subject
Wetenschapsjournalistiek (science journalism)
code
471014
co-ordinator
dr. K.T. Rebel
lecturers
dr. K.T. Rebel; drs L. Bonaparte; dr. H. van Maanen
credits
6
period
27.10.2008-21.11.2008
aim

Gaining insight in popularization of the beta sciences in print and digital
media;

Learning how to write popular science articles for newspapers,
magazines and websites;

Learning how to write specific genres like interviews, book reviews and
opinion articles.
content
This course consists of lectures about practical and theoretical aspects of
science journalism. Topics are the role of science journalism in constructing
relations between science and society, images of science in the press, ethical
aspects of science journalism and communication barriers between scientists
and journalists. Guest speakers give insight into their profession as science
j
ournalists, working for news-papers, magazines, internet or broadcasting
media. Moreover, you receive training in all aspects of writing popular
science articles, such as data collection (interviewing), writing techniques,
target groups and genres.
form of tuition
Lectures and seminars on theory and practice of science journalism and
writing skill training. Considerable time is set aside for writing popular
science articles. The assignments are assessed by lecturers and fellow
students.
literature
Donkers, H. & Willems, J. (2002). Journalistiek schrijven. Bussum:
Coutinho (2nd edition).
mode of assessment
Assessment is based on the last assignment (possibly adjusted on the basis of
the other assignments): a popular scientific article for a newspaper or
magazine.
target audience
Optional course in the C-differentiations (Science Communication) of most
of the two year master programs of FALW and FEW
remarks
Course is taught in Dutch.
For more information: karin.rebel@falw.vu.nl


Exam parts 47