SYLLABUS COURSE TITLE InteLligent computational techniques ...

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SYLLABUS



COURSE TITLE

INTELLIGENT COMPUTAT
IONAL TECHNIQUES

FACULTY/INSTITUTE

Faculty of Mathematics
and Natural Sciences
/ Institute

of Computer Science

COURSE CODE


DEGREE PROGRAMME

FIELD OF STUDY

DEGREE LEVEL

FORMA STUDIÓW/

STUDY MODE

COMPUTER SCI
ENCE

2

Full
-
time
studies


COURSE FORMAT


YEAR AND SEMESTER

1 year,

semester

I

NAME OF THE TEACHER

Prof.
Zbigniew Suraj
, PhD, DSc

COURSE OBJECTIVES

A student

obtain
s

a
theoretical understanding of the subject and the skill of solving simple
problems
coming from the design of intelligent systems

and data analysis
.

PREREQUISITES

Programmi ng, Arti fi ci al Intel l i gence,
Operati ng Systems,
Probabi l i ty Theory and Stati sti cs, Mathemati cal Anal ysi s,
Li near Al gebra
.


LEARNING OUTCOMES

Knowledge
:

(X1A_W01)

A
s
tudent
knows basic A
rtificial
I
ntelligence techni
ques
.
Moreover,

a
s
tudent knows different ways
of
knowledge representation
,
basic algorithms from rough
set
s, fuzzy sets and Petri nets.

Skills:



(X1A_U01, X1A_U03, X1A_U06, X1A_U08)

A s
tudent
is able t
o prepare data in a way required by
Data Mining algorithms.
A s
tudent
knows in what way
to apply rough set (fuzzy set, Petri net) methods for
solving basic problems com
ing from
intelligent system

domain

and data analysis
.


COURSE ORGANISATION


LEARNING
FORMAT AND NUMBER OF

HOURS


T
IMETABLE

Lecture:

Accordi ng to the schedul e
-

(30 hours)

Classes:

Accordi ng to the schedul e
-

(45 hours)



COURSE DESCRIPTION

Lecture:

Introduction:
an overvi ew of the fi el ds of Arti fi ci al I ntel l i gence and Concurrency.

Ro
ugh sets:

review of ordinary sets and relations, information tables and attributes, approximation spaces,
knowledge representation systems, case study and comparisons with other techniques
.

Fuzzy systems
:

fundamentals of fuzzy sets, basic fuzzy set relati
ons, basic fuzzy set operations and their
properties, fuzzy logic fundamentals, fuzzy control basics, a note on fuzzy control expert systems.

Classical
Petri nets
:

basic
concepts, particular Petri nets, properties of Petri nets, graph of markings and
cove
rabi l i ty tre
e
, l i n
ear al gebra, reducti on methods.

Non
-
classical Petri nets:
basi c concepts, parti cul ar non
-
cl assi cal Petri nets, exampl es of computer systems
supporti ng the net representati on of knowl edge and the model i ng of approxi mate reasoni ng.

Petri
nets and producti on rul e systems: a toy producti on rul e system, a data ori ented Petri net model, control
ori ented model s.


Classes:


Rough sets:

Data anal ysi s by
usi ng

the Rosetta and RSES systems.

Fuzzy systems:
Matl ab system i n the model i ng of fuzzy
con
trol expert
systems.

Petri nets: exampl es of own computer systems supporti ng the model i ng and anal ysi s of concurrent systems
(PN
-
tool s, PNES, ROSECON, etc.).


Non
-
classical Petri nets:
exampl es of own computer systems supporti ng the net
knowl edge
represe
ntati on and
the model i ng of approxi mate reasoni ng (PNES, APNES).



METHODS OF INSTRUCTI
ON


Lectures supported by sl i des and
speci al i zed software
.

REQUIREMENTS AND ASS
ESSMENTS


GRADING SYSTEM

L
ecture:


In order to pass

a

l ecture one needs:



to prepare a
mul ti medi a pres entati on (e.g. us i ng
PowerPoi nt appl i cation) rel ated to the chos en
topi c



to pas s

the

fi nal tes t

Grades:

Local grade: ECTS grade:

5 A (excel l ent)

4.5

B (very good)

4 C (good)

3.5 D+(pl us suffi ci ent)

3 D (suffi ci e
nt)

3
-

D (i nsuffi ci ent)

2 E (poor)

Classes:


Poi nts to gai n:



Presence record


max. 10 poi nts



Home
work



max. 20 poi nts (
5 home
work

each

for

4

poi nts)



T
ests


max. 30 poi nts (2 tests each
for
15 poi nts)



Maxi mum score


60 poi nts

Grades:

Local grade: ECTS grade:

5 A (excel l ent)

4.5

B (very good)

4 C (good)

3.5 D+(pl us suffi ci ent)

3 D (suffi ci ent)

3
-


D (i nsuffi ci ent)

2 E (poor)

TOTAL STUDENT WORKLO
AD NEEDED TO
ACHIEVE EXPECTED LEA
RNING OUTCOMES
EXPRESSED IN TIME AN
D ECTS CREDIT
POINTS

Lecture
:
80

Laborato
ry
:
50

ECTS


6

LANGU
AGE OF INSTRUCTION

Polish/English

(dependently on needs)

INTERNSHIP



MATERIALS

T
EXTBOOK AND
R
EQUIRED
M
ATERIALS
:


1.

K.

J.

C
IOS
,

W.

P
EDRYCZ
,

R.W.

S
WINIARSKI
,

D
ATA
M
INING
.

M
ETHODS FOR
K
NOWLEDGE
D
ISCOVERY
,

K
LUWER
A
CADEMIC
P
UBLISHERS
,

B
OSTON
1998.


2.

F
UZZINESS
IN
P
ETRI
N
ETS
,

J.C
ARDOSO
,

H.

C
AMARGO
(
EDS
.),

P
HYSICA
-
V
ERLAG
,

H
EIDELBERG
1999.


3.

R.

DAVID
,

H.

A
LLA
,

P
ETRI
N
ETS AND
G
RAFCET
.

T
OOLS FOR
MODELLING DISCRETE E
VENT SYSTEMS
,

P
RENTICE
H
ALL
,

N
EW
Y
ORK
1992.


4.

T.

M
UNAKATA
,

F
UNDAMENTALS OF THE
N
EW
A
RTIFICIAL
I
NTELLIGENC
E
.

B
EYOND
T
RADITIONAL
P
ARADIGMS
,

S
PRINGER
,

B
ERLIN
1998.