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Syllabus

Lect. Lecturer Topic Pdf
01 BF Convex optimisation I
02 BF Convex optimisation II
03 BF Markov Random Fields & Gibbs Random Fields
04 BF Estimating marginal probabilities
05 BF Estimating marginal probabilities
06 BF Maximum Likelihood learning for MRFs (supervised case)
07 BF Maximum Likelihood learning for MRFs (unsupervised case)
08 VF Discriminative structured output learning, Perceptron algorithm
09 VF Learning max-sum classifier by Perceptron
10 VF Structured Output Support Vector Machines
11 VF Batch Solvers for Convex Risk Minimization
12 VF Online Solvers for Convex Risk Minimization
13 BF Applications: Computer Vision
courses/xep33sml/materials/lectures.txt · Last modified: 2015/05/21 16:45 by xfrancv