=== Syllabus ===

^ Lect. ^ Lecturer ^ Topic ^ Pdf ^ Additional reading ^
| 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| }}| [[https://www.nowpublishers.com/article/Details/CGV-084| [Savchynskyy19] ]]|
| 03 | BF | Belief Networks & Stochastic Neural Networks| {{:courses:xep33sml:materials:lecture_03.pdf| }}| |
| 04 | VF | Empirical Risk Minimization | {{ :courses:xep33sml:materials:lecture_erm_ls2022.pdf | }}| |
| 05 | VF | Structured Output Linear Classifier and Perceptron| {{ :courses:xep33sml:materials:lecture_linclass_ls2022.pdf | }} | |
| 06 | VF | Learning max-sum classifier by Perceptron | {{ :courses:xep33sml:materials:lecture_maxsumperceptron_ls2022.pdf | }} | |
| 07 | VF | Structured Output Support Vector Machines | {{ :courses:xep33sml:materials:lecture_sosvm_ls2022.pdf | }} | |
| 08 | VF |Cutting Plane Algorithm | {{ :courses:xep33sml:materials:lecture_cpa_ls2022.pdf | }} | |
| 09 | VF | Learning Max-Sum classifier by SO-SVM  | {{ :courses:xep33sml:materials:lecture_sosvm4maxsum_ls2022.pdf | }} | |
| 10 | BF | Maximum Likelihood learning for MRFs I | {{:courses:xep33sml:materials:lecture_10.pdf| }}| updated on 4.5.22 |
| 11 | BF | Maximum Likelihood learning for MRFs II | {{:courses:xep33sml:materials:lecture_11.pdf| }} | |
| 12 | BF | Variational Autoencoders | {{:courses:xep33sml:materials:vae.pdf| }} | |