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	
GROSAN Crina Daniela	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	
Laura Diosan	Lecturer Dr.	Babes-Bolyai University			*	*	
Cristina Mihaila	Assistant Prof. 	Babes-Bolyai University			*	*	

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

IV. Course contents
1. Introduction to AI
2. Solving problems by search
3. Knowledge representation and reasoning
4. Fuzzy systems
5. Neural networks
6. Evolutionary computation
7. Swarm intelligence	

V. Bibliography
S. Russel, P. Norvig, Artificial Intelligence: A modern approach, Prentice Hall, Englewood Cliffs, NJ, 1995

VI. Thematic of didactic activities per weeks
1.	Introduction to AI
2.	Problem solving as search
o	Problem spaces
o	Uninformed search
•	Breadth first search
•	Depth first search
•	Limited depth first search
•	Iterative-deepening search
•	Uniform cost search
3.	Problem solving as search
o	Informed search
4.	Heuristic search
o	Best-first search 
o	Greedy 
o	A* algorithm 
o	A* variants
5.	Local search
o	Simulated annealing
o	Hill climbing
6.	Game playing
o	Minimax search
o	Alpha-beta pruning
7.	Knowledge representation and reasoning
o	Knowledge based systems
o	Rule based systems
8.	Uncertainty management in rule based systems. 
9.	Fuzzy systems
10.	Neural networks
o	Single layer neural networks
o	Perceptron model 
11.	Neural networks
o	Multi-layer neural networks
o	Backpropagation learning
o	Hopfield networks
o	Self organizing maps
12.	Evolutionary computation
o	Evolutionary algorithms
13.	Evolutionary Computation
o	Evolutionary strategies
o	Evolutionary programming
o	Genetic programming
14.	Swarm intelligence
o	Particle swarm optimization
o	Ant Colonies optimization	

VII. Didactic methods used
Exposition, discussions, student presentations, individual study, home-works, practical projects	

VIII. Assessment
The final mark will be given based on:
•	30% Lab assignment
•	60% Written exams (THERE WILL BE 3 WRITTEN EXAM ONE HOUR EACH DURING THE SEMESTER; EACH OF THEM WEIGTHS 20%)
•	10% Seminar project

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. GROSAN Crina Daniela, Ph.D.