IR2VS - Probability and Statistics
| Course specification | ||||
|---|---|---|---|---|
| Course title | Probability and Statistics | |||
| Acronym | IR2VS | |||
| Study programme | Electrical Engineering and Computing | |||
| Module | Computer Engineering and Informatics | |||
| Type of study | bachelor academic studies | |||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 6.0 | Status | elective | |
| Condition | Passed courses in Mathematics 1 and Mathematics 2 | |||
| The goal | - Introducing students to discrete and continuous models of probability theory, important distributions and methods of Statistics, including applications in Computer science. | |||
| The outcome | Knowledge of basic and moderately complex discrete and continuous models, with a view to applications in computer engineering and computer science. | |||
| Contents | ||||
| Contents of lectures | Introduction and combinatorics. Conditional probability and independence. Bayes theorem. Random variables and vectors. Information and entropy. Laws of large numbers. Central limit theorem. Estimation and hypotheses testing. Introduction to Monte Carlo methods. | |||
| Contents of exercises | Exercises in examples and problem solving. Project asignments in teams, with presentations. | |||
| Literature | ||||
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| Number of hours per week during the semester/trimester/year | ||||
| Lectures | Exercises | OTC | Study and Research | Other classes |
| 2 | 2 | 1 | ||
| Methods of teaching | Lectures, exercises with solving problems, homework and discussion. | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | 0 | Test paper | 50 | |
| Practical lessons | 0 | Oral examination | 0 | |
| Projects | 25 | |||
| Colloquia | 25 | |||
| Seminars | 0 | |||