=== Syllabus ===

^ Lect. ^ Lecturer ^ Topic ^ Pdf ^ Additional reading ^
| 01 | VF | Structured Output Linear Classifier and Perceptron| {{ :courses:xep33sml:materials:lecture_linclass_ls2022.pdf | }} | |
| 02 | VF | Learning max-sum classifier by Perceptron | {{ :courses:xep33sml:materials:lecture_maxsumperceptron_ls2022.pdf | }} | |
| 03 | VF | Structured Output Support Vector Machines | {{ :courses:xep33sml:materials:lecture_sosvm_ls2022.pdf | }} | |
| 04 | VF |Cutting Plane Algorithm | {{ :courses:xep33sml:materials:lecture_cpa_ls2022.pdf | }} | |
| 05 | VF | Learning Max-Sum classifier by SO-SVM  | {{ :courses:xep33sml:materials:lecture_sosvm4maxsum_ls2022.pdf | }} | |
| 06 | VF | Statistical guarantees for empirical risk minimization based SO learning | | |
| 07 | BF | Recap: Markov models and HMMs on chains | | |
| 08 | BF | Belief Networks & Probabilistic Neural Networks| {{:courses:xep33sml:materials:probabilistic-nn.pdf| }} |  [[https://openreview.net/forum\?id=SkMuPjRcKQ | A.Shekhovtsov, ICLR 2019]] |
| 09 | BF | Stochastic gradient estimators for PNNs| {{:courses:xep33sml:materials:sgd-prb-nn.pdf| }}|
| 10 | BF | Variational Autoencoders | {{:courses:xep33sml:materials:vae.pdf| }}| |
| 11 | BF | Hierarchical Variational Autoencoders | | |