Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Licenţă
Domeniul: Informatică
Programul de studii: Informatică - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MII0001	Artificial Intelligence	

Semester	Hours: C+S+L	Category	Status	
4	2+1+1	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
OLTEAN Mihai	Ph.D.	Lect.	Programming  Languages and Methods	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
Supply an introduction to the field of AI.
Give the basic notion, techniques ans algorithms of AI.
Suplly the background for advanced AI courses.	

IV. Course contents

V. Bibliography
[1].	All about AI on the net, www.aboutai.net
[2].	GOLDBERG, D. E., Genetic Algorithm. Addison-Wesley, Reading, 1989.
[3].	DUMITRESCU, D., Principiile Inteligentei artificiale, Editura Albastra, Cluj, 2000.
[4].	DUMITRESCU, D., Algoritmi genetici si strategii evolutive. Aplicatii in Inteligenta Artificiala, Editura Albastra, Cluj, 2000.
[5].	RUSSELL, S.J., NORVIG, P., Artificial intelligence. A modern approach, Prentice-Hall International, 1995.	

VI. Thematic of didactic activities per weeks
Schedule of lectures

What is AI?
	-References: 1, 2, 3, 4, 5,

Why AI is not possible?
-References: 1

Current achievements of AI
-References: 1, 5

AI and games
-References: 1

Evolutionary Computation
-References: 2, 4

Genetic Algorithms
-References: 2, 4

Genetic Programming
-References: 2, 4

Swarm Intelligence
-References: 1

Neural Networks
-References: 1, 3, 5

Expert Systems
-References: 1, 3, 5

Fuzzy Systems
-References: 1, 3, 5

AI for difficult problems
-References: 1, 5

Seminars

1.	Why we need AI?
-References: 1, 5

2.	Games and strategies
-References: 1

3.	Genetic Algorithms with real encoding 
-References: 2, 4

4.	Evolutionary Computation for NP-complete problems
-References: 2, 4

5.	Neural Networks implementation
-References: 1, 3

6.	Swam intelligence implementation
-References: 1

7.	Other AI techniques for difficult problems
-References: 1

Laboratories

1.	Presentation of the backtracking method and related problems
2.	Presentation of Genetic Algorithms for Optimization
3.	Presentation of Evolutionary Computation for NP-complete problems
4.	Presentation of other AI techniques for difficult problems / 1st lab must be presented to the adviser
5.	2nd lab must be presented to the adviser
6.	3rd lab must be presented to the adviser
7.	4th lab must be presented to the adviser

VII. Didactic methods used
Expunerea, conversatia, demonstratia didactica, algoritmizarea, problematizarea.	

VIII. Assessment
- Two written examinations during the semester. Together they will count 50% from the final mark.
- The other 50% will consist in 4 laboratories.

IX. Additional references

	Date,							Course responsible,
								Lect. OLTEAN Mihai, Ph.D.