SI2VS - Probability and Statistics
Course specification | ||||
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Course title | Probability and Statistics | |||
Acronym | SI2VS | |||
Study programme | Software Engineering | |||
Module | ||||
Type of study | bachelor academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | Passed exams in Mathematics 1 and Mathematics 2. | |||
The goal | Learning about discrete and continuous stochastic models and their applications, in particular in computer sciences. | |||
The outcome | A student will have a basic knowledge about simple and moderately complex discrete and continuous stochastic models and typical applications, particularly in computer sciences. He/she will be able to apply correct models in real life problems, and also to apply corresponding tools and procedures of statistics and probability. | |||
Contents | ||||
Contents of lectures | Combinatorial models. Conditional probability, independence. Bayes formula. Random variables and vectors. Information and entropy. Laws of large numbers. Central limit theorem. Estimation and hypotheses testing. Conditional distribution and linear regression. 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 | 30 hours of lectures and 30 hours of exercises in problem solving, 15 hours of discussions related to homework problems and preoject assignments. | |||
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 | 20 | |||
Colloquia | 30 | |||
Seminars | 0 |