=== Syllabus ===

^ Lect. ^ Lecturer ^ Topic ^ Pdf ^
| 01 | BF | Markov Random Fields & Gibbs Random Fields | {{:courses:xep33sml:materials:lecture_01.pdf| }}|
| 02 | BF | The most probable realisation of a GRF | {{:courses:xep33sml:materials:lecture_02.pdf| }}|
| 03 | BF | Belief Networks & Stochastic Neural Networks| {{:courses:xep33sml:materials:lecture_03.pdf| }}|
| 04 | VF | Discriminative structured output learning, Perceptron algorithm I| {{ :courses:xep33sml:materials:lecture_erm_2019.pdf| }} |
| 05 | VF | Discriminative structured output learning, Perceptron algorithm II| {{ :courses:xep33sml:materials:lecture_erm_2019.pdf| }} |
| 06 | VF | Learning max-sum classifier by Perceptron | {{ :courses:xep33sml:materials:lecture_maxsumperceptr_2019.pdf| }}  |
| 07 | VF | Structured Output Support Vector Machines I | {{ :courses:xep33sml:materials:lecture_sosvm_2019.pdf | }} |
| 08 | VF | Structured Output Support Vector Machines II | {{ :courses:xep33sml:materials:lecture_sosvm_2019.pdf | }} |
| 09 | VF | Learning Max-Sum classifier by SO-SVM  | {{ :courses:xep33sml:materials:lecture_sosvmmaxsum_2019.pdf | }} +[[https://www.cs.cornell.edu/people/tj/publications/joachims_05a.pdf|Joachims05 ]]|
| 10 | BF | Maximum Likelihood learning for MRFs I |{{ :courses:xep33sml:materials:lecture_10.pdf | }} |
| 11 | BF | Maximum Likelihood learning for MRFs II | {{ :courses:xep33sml:materials:lecture_11.pdf | }}|
| 12 | BF | Variational Bayesian inference for DNNs | {{ :courses:xep33sml:materials:bayes-inf.pdf | }}|
| 13 | BF | Variational Autoencoders | {{ :courses:xep33sml:materials:vae.pdf | }}|
| 14 | BF | Generative adversarial networks | {{ :courses:xep33sml:materials:gans.pdf | }}|