Study program: F3 Computer Science, Educational and professional program "Computer Science and Technology”

Degree: Bachelor

Type of module: lectures, practical work

Lecturer: Professor, Doctor of Science Igor Grebennik

Language: English

Credit Points: 5 ECTS

Description:
Mandatory discipline of basic (professional) training by specialty, contains content topics:
1. Basic Ideas in Probability. Describing Datasets.
2. Graphical Representing and Summarizing 1D Data.
3. Plotting and Analysis of Relationship for 2D Data. Correlation Coefficient.
4. Probability and its calculation.
5. Conditional Probability. Law of Total Probability. Bayes’ theorem.
6. Random Values, their Distributions and Numerical Characteristics.
7. Distributions of Random Values. Important Cases.
8. Normal Distribution and connected Expressions.
9. Systems and Functions of Random Values. Central limit theorem.
10. Problems of Mathematical Statistics. Statistical inference.
11. Confidence Intervals. Student’s t-distribution.
12. The Significance of Evidence.
13. Evaluating Significance. The p-value.
14. The T-Test of Significance for a Hypothesized Mean.
15. Comparing the Mean of Two Populations.
16. Statistical Hypotheses of Model Fit. Chi-square Test.
17. Least squares method.
18. Probabilistic Processes.

Practice topics:
1. Plotting Data.
2. Basic Ideas in Probability. Independence. Conditional probability.
3. Total Probability. Discrete random variable.
4. Probability distribution of discrete random value.
5. Continues random variable and its distributions.
6. Confidence intervals. Pearson's criteria chi-square.