Course aims:

This course is designed to introduce students to various topics in probability and uncertainty that they will encounter in Computer Science theory. The concepts are illustrated with actual examples from the specialized literature.
Exercises are designed to encourage the student to begin thinking about probability within a theoretical context. Today, the theory of probability has found many applications in science and engineering.
In this course, the students will learn the basic terminology and concepts of probability theory and statistics.

Course Entry Requirements:

Linear Algebra

Course contents:

1. Field of events 2. Probability field 3. Rules for assigning and calculating probabilities 4. Classical probability distributions 5. Discret random variables 6. Continuous random variables 7. Numerical characteristics of random variables 8.The characteristic function. Moment generating function 9. The law of large numbers for random variables. Limit theorems 10. Statistical selection theory 11. Glivenko’s theorem. Kolmogorov’s theorem 12. Estimation theory 13. Confidence intervals 14. Statistical hypothesis testing

Teaching methods:

Lecture, conversation, exemplification.

Learning outcomes:

Modelling and solving some medium complexity level problems, using the mathematical and economics knoweledges.

Learning outcomes verification and assessment criteria:

Written paper 50%; mid-term test 30%; seminar activities 20%.

Recommended reading:

-, • Micula, S., Probability and Statistics for Computational Sciences, Cluj University Press, 2009, -, -, -, -.
-, • Agratini, O., Blaga, P., Coman, Gh., Lectures on Wavelets, Numerical Methods and Statistics, Casa Cartii de Stiinta, Cluj-Napoca, 2005., -, -, -, -.
-, • Feller, W., An introduction to probability theory and its applications, Vol.I-II, John Wiley, New York, 1957, 1966., -, -, -, -.