Fundamentals of Probability Theory for MSc Data Science
WS 2026/27
| Lecture: | Monday, 14:00-16:00 in M5, Einsteinstr. 64. First lecture takes place on October 12, 2026. |
| Tutorial: | tba, probably Friday 10:00-12:00 in SRZ 204 |
| Lecturer: | Prof. Dr. Zakhar Kabluchko |
| Assistant: | Philipp Schange |
| Content: |
This lecture is an introduction to probability theory for students of the new Master’s program in Data Science. Key topics include: discrete probability theory, conditional probabilities (law of total probability, Bayes’ theorem, etc.), random variables, expected value, variance; probability distributions and their properties (Bernoulli, binomial, hypergeometric, Poisson, geometric and normal distributions), modelling using such distributions, the law of large numbers, the central limit theorem, estimation methods and elementary hypothesis testing. |
| KommVV: | The course in the course overview The tutorials in the course overview |
| Learnweb: |
If you intend to participate in this course, please register in the learnweb for this course Link |