syllabus
1. Information regarding the programme
1.1 Higher education institution 	Babeş Bolyai University	
1.2 Faculty	Faculty of Mathematics and Computer Science	
1.3 Department	Department of Computer Science	
1.4 Field of study	Computer Science	
1.5 Study cycle	Bachelor	
1.6 Study programme / Qualification 	Computer Science	

2. Information regarding the discipline 
2.1 Name of the discipline	Artificial Intelligence	
2.2 Course coordinator 	Lecturer PhD. Crina Grosan	
2.3 Seminar coordinator	Lecturer PhD. Crina Grosan	
2.4. Year of study	2	2.5 Semester	4	2.6. Type of evaluation	E	2.7 Type of discipline	Compulsory	

3. Total estimated time (hours/semester of didactic activities) 
3.1 Hours per week 	4	Of which: 3.2 course	2	3.3 seminar/laboratory	1 sem+ 1 lab	
3.4 Total hours in the curriculum 	56	Of which: 3.5 course	28	3.6 seminar/laboratory	28	
Time allotment:	hours	
Learning using manual, course support, bibliography, course notes	28	
Additional documentation (in libraries, on electronic platforms, field documentation) 	14	
Preparation for seminars/labs, homework, papers, portfolios and essays	28	
Tutorship	7	
Evaluations	20	
Other activities: ..................	-	
3.7 Total individual study hours 	70	
3.8 Total hours per semester	153	
3.9 Number of ECTS credits	6	

4. Prerequisites (if necessary)
4.1. curriculum	•	Graph Theory, Data Structures and Algorithms	
4.2. competencies	•	Average programming skills in a high level programming language	

5. Conditions (if necessary)
5.1. for the course	•		
5.2.  for the seminar /lab activities	•	Laboratory with computers; high level programming language environment 	

6. Specific competencies acquired 
Professional competencies	•	An introduction to the field of AI.
•	Acquire the basic notion, techniques and algorithms of AI.
•	The background for advanced AI courses.	
Transversal competencies	•	Ability to apply AI techniques to different real life problems
•	Ability to model problems in an interdisciplinary field

7. Objectives of the discipline (outcome of the acquired competencies)
7.1 General objective of the discipline
	•	Ability to understand and use the basic AI algorithms and principles.
•	Ability to model real life problems as AI problems and find optimal solutions to them	
7.2 Specific objective of the discipline
	•	Acquire the knowledge about the main classes of soft computing algorithms, the basic notions of game theory and knowledge base reasoning.	

8. Content
8.1 Course	Teaching methods	Remarks	
1.	Introduction to AI	Exposure: description, explanation, examples, discussion of case studies		
2.	Problem solving as search
Problem spaces
Uninformed search
Breadth first search
Depth first search
Limited depth first search
Iterative-deepening search
Uniform cost search	Exposure: description, explanation, examples, discussion of case studies		
3.	Problem solving as search
Informed search
Heuristic search
Best-first search 
Greedy 
A* algorithm       
A* variants	Exposure: description, explanation, examples, debate, dialogue		
4.	Local search
Simulated annealing      
Hill climbing	Exposure: description, explanation, examples, discussion of case studies		
5.	Game playing
Minimax search
Alpha-beta pruning
	Exposure: description, explanation, examples, proofs		
6.	Knowledge representation and reasoning
Knowledge based systems
	Exposure: description, explanation, examples, proofs, debate, dialogue		
7.	Rule based systems
Uncertainty management in rule based systems. 
	Exposure: description, explanation, examples, discussion of case studies		
8.	Fuzzy systems
	Exposure: description, explanation, examples		
9.	Neural networks
Single layer neural networks
Perceptron model 
	Exposure: description, explanation, examples, discussion of case studies		
10.	Neural networks
Multi-layer neural networks
Backpropagation learning
	Exposure: description, explanation, examples, debate		
11.	Hopfield networks
Self organizing maps
	Exposure: description, explanation, examples, discussion of case studies		
12.	Evolutionary computation
Evolutionary algorithms
	Exposure: description, explanation, examples, discussion of case studies		
13.	Evolutionary Computation
Evolutionary strategies
Evolutionary programming
Genetic programming
	Exposure: description, explanation, examples, discussion of case studies		
14.	Swarm intelligence
Particle swarm optimization
Ant Colonies optimization	Exposure: description, examples, discussion of case studies, live demo		
Bibliography
GROSAN, C., Abraham, A., Intelligent Systems: a modern approach, Springer Verlag GERMANY, 2011
PATRIDGE, D., Artificial Intelligence. Aplications in the future of software engineering, Ellis Harwood Series in A.I., John Wiley & Sons, New York 1986.
RICH, E. Artificial Intelligence, Mc.Graw Hill, 1989.
WINSTON, P., Inteligenta artificiala, Ed.Tehnica, 1980.
GOLDBERG, D. E., Genetic Algorithm. Addison-Wesley, Reading, 1989.	
8.2 Seminar	Teaching methods	Remarks	
1.	Introduction to AI: A Turing and it’s role, the Darmouth College Conference	Explation, dialogue, case studies	The seminar is structured as 2 hours classes every second week	
2.	Informed, Uninformed, Local search	Dialogue, debate, case studies, examples, proofs		
3.	Games	Dialogue, debate, case studies, examples, proofs		
4.	Fuzzy systems: construction	Dialogue, debate, case studies, examples		
5.	Neural networks: example and applications	Dialogue, debate, case studies, examples		
6.	Genetic algorithms: examples and problem solving	Dialogue, debate, case studies, examples		
7.	Swarm intelligence examples and applications	Dialogue, debate, case studies, examples		
Bibliography
1.	GROSAN, C., Abraham, A., Intelligent Systems: a modern approach, Springer Verlag GERMANY, 2011
2.	RUSSELL, S., J., NORVIG, P., Artificial intelligence: A modern approach, N.J. Prentice Hall/Pearson Education, 2003	
8.3 Laboratory	Teaching methods	Remarks	
1.	Task: Discuss the main principles of AI, first research publications and contributions
	case studies, dialogues		
2.	Task: Implement a search algorithm, given from a list of projects
	case studies, testing		
3.	Task: solve (implemnet and test) a game, given from a list of projects
	case studies, testing		
4.	Task: implement a fuzzy sistem
	case studies, testing		
5.	Task: implement a neural network and test it for a given problem
	case studies, testing		
6.	Task: implement a genetic algorithm and test it for a given problem
	case studies, testing		
7.	Task: inplement a swarm intelligence algorithm and test it for an optimization problem
	case studies, testing		
Bibliography
1.	GROSAN, C., Abraham, A., Intelligent Systems: a modern approach, Springer Verlag GERMANY, 2011
2.	RUSSELL, S., J., NORVIG, P., Artificial intelligence: A modern approach, N.J. Prentice Hall/Pearson Education, 2003	

9. Corroborating the content of the discipline with the expectations of the epistemic community, professional associations and representative employers within the field of the program

•	The course follows the scheme and structure used by the most important universities in USA and Europe;
•	The course exists in the studying program of all major universities in Romania and abroad;

10. Evaluation
Type of activity	10.1 Evaluation criteria	10.2 Evaluation methods	10.3 Share in the grade (%)	
10.4 Course	- know the basic principle of the AI domain;
- apply the course concepts
- problem solving	Written exam (there will be two written exams)
	60%	
10.5 Seminar/lab activities	- be able to implement the algorithm described in the course and discussed during the seminars
	-Lab assignments	40%	
10.6 Minimum performance standards	
•	At least grade 5 (from a scale of 1 to 10) at both written exams and laboratory work.	

Date		Signature of course coordinator 		Signature of seminar coordinator
..................	Lecturer PhD. Crina Grosan			Lecturer PhD. Crina Grosan
Date of approval  					  Signature of the head of department	
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