=== Syllabus ===

^ Lect. ^ Lecturer ^ Topic ^ Pdf ^
| 01 | BF | Convex optimisation I|{{:courses:xep33sml:materials:lecture_01.pdf| }} |
| 02 | BF | Convex optimisation II|{{:courses:xep33sml:materials:lecture_02.pdf| }} |
| 03 | BF | Markov Random Fields & Gibbs Random Fields| {{:courses:xep33sml:materials:lecture_03.pdf| }}|
| 04 | BF | Estimating marginal probabilities| {{:courses:xep33sml:materials:lecture_04.pdf| }}|
| 05 | BF | Estimating marginal probabilities| {{:courses:xep33sml:materials:lecture_05.pdf| }}|
| 06 | BF | Maximum Likelihood learning for MRFs (supervised case)| {{:courses:xep33sml:materials:lecture_06.pdf| }}|
| 07 | BF | Maximum Likelihood learning for MRFs (unsupervised case)| {{:courses:xep33sml:materials:lecture_07.pdf| }}|
| 08 | VF | Discriminative structured output learning, Perceptron algorithm| {{:courses:xep33sml:materials:lecture_08.pdf| }}|
| 09 | VF | Learning max-sum classifier by Perceptron | {{:courses:xep33sml:materials:lecture_09.pdf| }} |
| 10 | VF | Structured Output Support Vector Machines| {{:courses:xep33sml:materials:lecture_10.pdf| }}|
| 11 | VF | Batch Solvers for Convex Risk Minimization | {{:courses:xep33sml:materials:lecture_11.pdf| }}|
| 12 | VF | Online Solvers for Convex Risk Minimization | {{:courses:xep33sml:materials:lecture_12.pdf| }} |
| 13 | BF | Applications: Computer Vision| {{:courses:xep33sml:materials:lecture_13.pdf| }} |