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

SYLLABUS

I. General data
Code	Subject	
MI371	Advanced Methods in Data Analysis	

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

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
POP Horia Florin	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
To introduce the student in advanced methods of data analysis. To offer the student the instruments that will allow him/her to develop different data analysis applications.	

IV. Course contents
1. Administrivia
2. Introduction to Data Mining	
3. Fuzzy sets
4. Fuzzy logic; Fuzzy reasoning
5. Fuzzy control systems
6. Rough sets; Decision tables
7. Decision trees; Association rules
8. Neural networks; Genetic algorithms
9. Methods for prediction
10. Principal components, Factor analysis
11. Classification; Clustering
12. Feature extraction; 
13. Performance analysis
14. Text mining, Web mining
15. Applications of data analysis	

V. Bibliography
[1] J. Han, M. Kamber, Data Mining: Concepts and Techniques, Academic Press, 2001 
[2] G.J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic, Prentice Hall, 1995
[3] T. Mitchell, Machine Learning, McGraw Hill, 1996
[4] Z. Pawlak, Rough Sets, Polish Academy of Sciences, Gliwice, 2004
[5] N. Ye, The Handbook of Data Mining, Lawrence Elbaum Associates Publishers, 2003 

Additional references

[1] A. Agresti, An Introduction to Categorical Data Analysis, Wiley, New York, 1996
[2] M. Barthold, D.J. Hand, Intelligent Data Analysis, Springer Verlag, 2003
[3] C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995 
[4] J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Kluwer, 1981 
[5] Y.H. Pao, Adaptive pattern recognition and neural networks, Addison Wesley, 1989 
[6] Statsoft inc., Electronic Statistics Textbook, Tulsa, OK, 2004, http://www.statsoft.com
[7] Internet resources	

VI. Thematic of didactic activities per weeks

VII. Didactic methods used

VIII. Assessment
Each student has to prove that (s)he acquired an acceptable level of understanding and processing of the domain knowledge, that (s)he is able of expressing this knowledge in a coherent form, that (s)he has the ability to develop a conceptual analysis of the domain and to use the knowledge in problems solving. The final grade is computed as follows: 10% - Class attendance and participation; 20% + 20% - Two reports (written and presented on time); 20% - Software project (written, documented and demonstrated in time); 30% - Final exam (written paper in exams session). All elements are compulsory. The course web page: http://www.cs.ubbcluj.ro/~hfpop/amda	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Prof. POP Horia Florin, Ph.D.