Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: 
Programul de studii: Scoala doctorala
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MID2010	Metode avansate de instruire automată	

Semester	Hours: C+S+L	Category	Status	
2	2+1+0+0		compulsory	

II Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
CZIBULA Gabriela	Ph.D.	Prof.	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
1. To provide an introduction to the basic principles, techniques, and applications of Machine Learning.
2. To cover the principles, design and implementation of learning programs which improve their performance on some set of tasks by experience.
3. To offer a broad understanding of machine learning algorithms and their use in data-driven knowledge discovery and program synthesis.
4. To offer an understanding of the current state of the art in machine learning in order to conduct original research in machine learning.

IV. Course contents
1. Introduction in Machine Learning. Statistical Foundations. 
2. Decision Tree learning. 
3. Artificial Neural Networks. 
4. Support Vector Machines. 
5. Bayesian Learning. 
6. Instance based learning. 
7. Unsupervised learning. 
8. Reinforcement Learning.	

V. Bibliography
1.	Mitchell, T., Machine Learning, McGraw Hill, 1997 
2.	Russell, J.S, Norvig, P., Artificial Intelligence- A Modern Approach, Prentice- Hall, Inc., New Jersey, 1995
3.	Gabriela Czibula, Sisteme inteligente. Instruire automata, Ed. Risoprint, Cluj-Napoca, 2008 
4.	Manning, C., Schutze, H., Foundations of Statistical NLP, MIT Press, 2002 
5.	Cristiani, N., Support Vector and Kernel Machines, BIOwulf Technologies, 2001
6.	 Nillson, N., Introduction to Machine Learning, Stanford University, 1996

VI. Thematic of didactic activities per weeks
1.	Introduction in Machine Learning. Statistical Foundations  [1, ch.1, 3, ch. 2] (2 lectures – S1, S2 + 2 seminars – S1, S2)
•	Issues in Machine Learning
•	Designing a learning system
•	Event space and Probability function
•	Elementary Information Theory
2.	Decision Tree learning  [1, ch.3] (1 lecture – S3 + 1 seminar – S3)
•	Decision tree representation
•	ID3 learning algorithm
•	Statistical measures in decision tree learning: entropy, information gain
•	Issues in DT learning
•	Applications
3.	Artificial Neural Networks  [1, ch.4] (2 lectures – S4, S5 + 2 seminars – S4, S5)
•	Neural Network representations
•	Appropriate problems for Neural Network Learning
•	Perceptrons
•	Multilayer Networks and the Backpropagation algorithm
•	Advanced topics in Artificial Neural Networks
4.	Support Vector machines  [4] (2 lectures – S6, S7 + 2 seminars – S6, S7)
•	Main idea
•	Linear SVMs
•	Non-linear SVMs
•	Applications
5.	Bayesian learning  [1, ch. 6] (2 lectures – S8, S9 + 2 seminars – S8, S9)
•	Bayes theorem
•	Naive Bayes Classifier
•	Bayesian Belief Networks
•	EM algorithm
•	Examples
6.	Instance based learning  [1, ch. 8] (2 lectures – S10, S11 + 2 seminars – S10, S11)
•	k-Nearest Neighbor learning
•	Locally weighted regression
•	Radial basis functions
•	Case based reasoning
7.	Unsupervised Learning  [5, ch. 9] (1 lecture – S12 + 1 seminar – S12)
•	Cluster analysis
•	Self organizing maps
•	Hebbian learning
•	Applications 
8.	Reinforcement Learning  [1, ch. 13] (2 lectures – S13, S14)
•	The reinforcement learning task
•	Markov Decision Processes
•	Q-learning
•	Temporal Difference learning
•	Applications

Seminars S13, S14 – presentation of semester projects	

VII. Didactic methods used
Lectures, conversations, projects, exercises, individual study, homework assignments.	

VIII. Assessment
The activity ends with a written final exam (grade E). During the semester, the students will have to prepare a theoretical report (grade R) and a practical project that will have to illustrate a learning task (grade P). The seminar activity will also be graded (grade L). The students activity during the semester will be also considered (grade A).  The final grade is the weighted mean of the five grades mentioned above. The final grade = 40%E + 20%R + 15%P + 15%L+ 10%A. Students who demonstrate excellent research performance by developing the project to publication will get an extra score of 10% from the final grade. Successful passing of the exam is conditioned by the final grade that has to be at least 5. More about evaluation can be found at http://www.cs.ubbcluj.ro/~gabis/Req_ML.htm.

IX. Additional references
1.	Sutton, R.S., Barto, A.G., Reinforcement learning, The MIT Press Cambridge, Massachusetts, London, England, 1998 
2.	Şerban, G., Pop, H.F., Tehnici de Inteligenţă Artificială. Abordări bazate pe Agenţi Inteligenţi, Ed. Mediamira, Cluj-Napoca, 2004 
3.	Şerban, G., Sisteme multiagent în Inteligenţa Artificială Distribuită. Arhitecturi şi aplicaţii. Editura RisoPrint, Cluj-Napoca, 2006
4.	Internet resources	

	Dean,							Chair head,

	Date,							Course responsible,
	1.10.2010						Prof. CZIBULA Gabriela, Ph.D.