Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Licenţă
Domeniul: Calculatoare şi tehnologia informaţiei
Programul de studii: Ingineria informatiei - in limba engleza
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MMP0006	Information Theory	

Semester	Hours: C+S+L+P	Category	Status	
5	2+2+0+0	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
TRÎMBITAS Radu Tiberiu	Ph.D.	Assoc.Prof.	Applied Mathematics	*			

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

III. Course objectives
Introduction in information theory. We will give the concepts of information transmission and processing.	

IV. Course contents
Basics on Measure Theory. Probabilistic Models.
Random Variable. Distribution Function. Density Function. Numerical Characteristics of Random Variables. Expectation, Variance, Standard Deviation. Correlation. 
Conditional Probability.
Chebyshev Inequality. Stochastic Convergence. The Weak Laws of Large Numbers.
Central Limit Theorems.
Stochastic Processes. Markov Chains, Ergodicity.
The Notion of Information. Shannon Information.
Entropy and its Properties.
Information Resources.   Ergodicity and nonergodicity.
Discrete and Continuous Resources.
Coding Theory.
Applications

V. Bibliography
1.	Gray R.M., Entropy and Information Theory, Springer, 1999
2.	Blaga, P., Calculul probabilităţilor şi statistică matematică. Vol. II. Curs şi culegere de probleme, UBB, Cluj-Napoca, 1994.
3.	Blaga, P., Statistica... prin Matlab, Presa Universitară Clujeană, Cluj-Napoca, 2002.
4.	Murgan A. T., Principiile teoriei Informatiei in ingineria informatiei si a comunicatiilor, Editura Academiei Romane, Bucuresti, 1998
5.	Soós A., Bevezetés a valószínűségszámításba, Presa Universitara Clujeana, 2001. 

VI. Thematic of didactic activities per weeks
W1. Basics on Measure Theory. Probabilistic Models.
W2-3. Random Variable. Distribution Function. Density Function. Numerical Characteristics of Random Variables. Expectation, Variance, Standard Deviation. Correlation. 
Conditional Probability.
W4. Chebyshev Inequality. Stochastic Convergence. The Weak Laws of Large Numbers.
W5. Central Limit Theorems.
W6. Stochastic Processes. Markov Chains, Ergodicity.
W7. The Notion of Information. Shannon Information.
W8. Entropy and its Properties.
W9. Information Resources.   Ergodicity and nonergodicity.
W10. Discrete and Continuous Resources.
W11-12. Coding Theory. Error Corrector and Detector –Codes.
W13-14. Applications

VII. Didactic methods used
Exposition, Conversations, Use of problems, Discovery, Individual study, Exercise, Homework study.	

VIII. Assessment
The final note will be given by:
seminary work 40%
exam 60%	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
							Assoc.Prof. TRÎMBITAS Radu Tiberiu, Ph.D.