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| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students can conceive of, and formalise, large classes of problems in AI and machine learning in probabilistic terms. They have a good understanding of what kinds of problems are suitable for probabilistic modeling approaches, and what assumptions need to be made. They understand the mindset of Bayesian approaches to probabilistic modeling, inference, and learning, and on this basis can extend their own knowledge about AI and machine learning by reading and making sense of the latest scientific literature.
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| Fertigkeiten |
Kenntnisse |
| (*)Students understand the fundamental concepts and rules of probability (k2), and can manipulate mathematical probability statements in useful ways (k3). They can implement fundamental algorithms related to probabilistic inference and model learning (k4).
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(*)Elementary concepts of probability: distributions, density functions, (conditional) independence, factorisation. Definition and semantics of several classes of probabilistic models (Bayesian Networks, Hidden Markov Models, Kalman Filters, Linear Regression); algorithms for exact and approximate inference in structured probabilistic models; fundamental concepts of inductive learning of parameters and model structure from data; selected examples of applications of probabilistic graphical models to non-trivial problems (including real-time inference tasks).
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| Beurteilungskriterien |
(*)Written exam at the end of the semester.
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| Lehrmethoden |
(*)Lecture series with written materials (presentation slides) provided regularly in electronic form.
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Koller, Daphne, and Friedman, Nir (2009). Probabilistic Graphical Models. Principles and Techniques. Cambridge, MA: MIT Press.
Russell, Stuart J. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd Edition). Upper Saddle River, NJ: Prentice Hall.
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)The lecture series (VL) and the corresponding exercise course (UE) form a didactic unit. The study results described here are achieved through the combination of these two courses.
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