13E082VS - Probability and Statistics
| Course specification | ||||
|---|---|---|---|---|
| Course title | Probability and Statistics | |||
| Acronym | 13E082VS | |||
| Study programme | Electrical Engineering and Computing | |||
| Module | ||||
| Type of study | bachelor academic studies | |||
| Lecturer (for classes) | ||||
| Lecturer/Associate (for practice) | ||||
| Lecturer/Associate (for OTC) | ||||
| ESPB | 6.0 | Status | elective | |
| Condition | Mathematics 1 and Mathematics 2 (passed exams). | |||
| The goal | Understanding applications of methods of Probability and Statistics in solving problems from real world, in particular in engineering problems. Enabling students to solve various real world problems, applying the metods of Probability and Statistics, determining the correct mathematical model, and performing related parameter estimation and testing hypotheses | |||
| The outcome | A student will be familiar with basic and moderately complex discrete and continuous stochastic models. He/she will be able to apply correct models in real life problems, and to apply tools and methods of statistics and probability. | |||
| Contents | ||||
| URL to lectures | https://teams.microsoft.com/l/team/19%3a5OYm76XxRKHnPHdQgMmM6_9tyZdSHv0HkRDFHiJkU6E1%40thread.tacv2/conversations?groupId=ac1d0eda-5da8-42d7-9e5b-bb2599517817&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba | |||
| Contents of lectures | Introduction and combinatorics. Conditional probability. Bayes formula and applications. Random variables. Numerical characteristics. Random vectors. Characteristic functions. Limit theorems. Parameter estimation. Hypotheses testing. Conditional distributions. Linear regression. Monte Carlo methods. | |||
| Contents of exercises | Lab exercises: Solving problems in Probability and Statistics by methods of simulations, and graphical presentations of results. | |||
| 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 | 30 hours of theoretical lecturing, 30 hours of exercises, 15 hours of lab. | |||
| Knowledge score (maximum points 100) | ||||
| Pre obligations | Points | Final exam | Points | |
| Activites during lectures | 0 | Test paper | 70 | |
| Practical lessons | 6 | Oral examination | 0 | |
| Projects | 0 | |||
| Colloquia | 24 | |||
| Seminars | 0 | |||