Course outline - Suraj @ LUMS

journeycartΤεχνίτη Νοημοσύνη και Ρομποτική

15 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

111 εμφανίσεις

CS/CMPE 535


Machine Learning

Outline

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

2

Description


A course on the fundamentals of machine learning


the science of designing and implementing adaptive
systems


Concept learning


Inductive learning and decision trees


Computational learning theory


Bayesian learning


Graphical models



Emphasis on fundamental mathematical and conceptual
understanding


Significant exposure to real
-
world implementations and
applications

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

3

Goals


To provide a comprehensive introduction to machine
learning methods


To build mathematical foundations of machine
learning and provide an appreciation for its
applications


To provide experience in the implementation and
evaluation of machine learning methods


To develop research interest in the theory and
application of machine learning

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

4

Machine Learning is ….


Essential for those who want to specialize in artificial
intelligence and/or want to pursue research in data
mining, machine learning, robotics, computer vision,
and computer networks


Strongly recommended for all graduate students
interested in research


Recommended for students with applied sciences
backgrounds such as engineering

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

5

Before Taking This Course…

You should be comfortable with…


Probability!


MATH 131 is a prerequisite


Please revise and keep handy the notes from this course


Artificial intelligence


General conceptual understanding would be of much help


CS331/CS531 is recommended, not required


Programming


MATLAB


C/C++ or Java


CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

6

Grading


Points distribution




Quizzes (~ 7)





15%


Assignments (hand + computer)


20%


Midterm exam





30%


Final exam (comprehensive)



35%

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

7

Policies (1)


Quizzes


Most

quizzes will be announced a day or two in advance


Unannounced quizzes are also possible


Sharing


No copying is allowed for assignments. Discussions are
encouraged; however, you
must

submit your own work


Violators can face mark reduction and/or reported to
Disciplinary Committee


Plagiarism


Do NOT pass someone else’s work as yours! Write in your
words and cite the reference. This applies to code as well.

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

8

Policies (2)


Submission policy


Submissions are due at the day and time specified


Late penalties: 1 day = 10%; 2 day late = 20%; not accepted
after 2 days


An extension will be granted only if there is a
need

and when
requested
several days

in advance.


Classroom behavior


Maintain classroom sanctity by remaining quiet and attentive


If you have a need to talk and gossip, please leave the
classroom so as not to disturb others


Dozing is allowed provided you do not snore loud




CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

9

Policies (3)


Attendance


Although attendance is not recorded and graded (in general)
it is strongly recommended. Otherwise, you will miss out on
key understandings not explicitly covered in the textbook


This recommendation is based on experience of previous
courses

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

10

Summarized Course Contents


Introduction, motivation, and applications


Concept learning


Decision tree learning


Evaluating hypotheses and computational learning
theory


Bayesian learning


Graphical models




CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

11

Course Material


Required textbook


T. Mitchell, Machine Learning, McGraw
-
Hill, 1997.


Recommended supplementary text


E. Alpaydin, Introduction to Machine Learning, Pearson
Education, 2004.


C. Bishop, Pattern Recognition and Machine Learning,
Springer, 2006.


Other material


Handouts (papers and tutorials as and when necessary)


Other resources


Books in library


Web

CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

12

Course Web Site


For announcements, lecture slides, handouts,
assignments, quiz solutions, web resources:



http://suraj.lums.edu.pk/~cs535w07/



The resource page has links to information available on
the Web. It is basically a meta
-
list for finding further
information.


CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

13

Other Stuff


How to contact me?


Office hours: 11.40 to 13.10 MW (office: 429)


E
-
mail:
akarim@lums.edu.pk


By appointment: to see me outside the office hours e
-
mail me
for an appointment before coming


Philosophy


Knowledge cannot be taught; it is learned.


Be excited. That is the best way to learn. I cannot teach
everything in class. Develop an inquisitive mind, ask
questions, and go beyond what is required.


I don’t believe in strict grading. But… there has to be a way
of rewarding performance.


CS 535
-

Machine Learning (Wi 2007
-
2008)
-

Asim Karim @ LUMS

14

Reference Books in LUMS Library


There are numerous books on machine learning and
related topics in the library.


Browse the library holdings to get a feel of the books


Search the library portal using keywords like “machine
learning”, “learning”, “statistical learning”, etc