Materials
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| 2020-1104: Introduction (3.6 MB, rev 1) | ||
| 2020-1111: Bayesian and Markov Networks (1.3 MB, rev 1) | ||
| 2020-1125: Factor Graphs and Sum-Product Inference (2.6 MB, rev 1) | ||
| 2020-1202: Max-Product Inference and Pose Estimation (12 MB, rev 1) | ||
| 2020-1209: Image Procesing (4.8 MB, rev 1) | ||
| 2021-0106: Dense CRF (9.1 MB, rev 1) | ||
| 2021-0113: GNN Intro (7.4 MB, rev 1) | ||
| 2021-0120: GNNs Part 2 (10 MB, rev 1) |
| 2020-1106: Matlab Intro (2.6 MB, rev 1) | ||
| Assignement2_data (35 MB, rev 1) | ||
| Assignment3_data (16 KB, rev 1) | ||
| Assignment 1 (166 KB, rev 1) | Due date: 25th Nov, 11:59pm | |
| Assignment 2 (183 KB, rev 1) | Due date: 13th Dec, 11:59pm | |
| Assignment 3 (147 KB, rev 1) | Due date: 4th Jan (2021), 11:59pm |
| MatLab installation | in german |
| Bayesian Reasoning and Machine Learning | David Barber, Cambridge University Press, available online | |
| Discrete Graphical Models - An Optimization Perspective | Bogdan Savchynskyy, preprint, available online | |
| Pattern Recognition and Machine Learning | Chris Bishop, Springer, 2006 | |
| Probabilistic Graphical Models: Principles and Techniques | Daphne Koller and Nir Friedman, MIT Press, 2009 (careful: 1300 pages) |
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