Warning
This page is located in archive.

Syllabus

Lecture Date Topic Lecturer Pdf Notes
1.2.10. Introduction BF
2.9.10. Empirical risk minimization I VF chap 2 in [1]
3.16.10. Empirical risk minimization II VF chap 3 in [1]
4.23.10. Support Vector Machines VF chap 4, 5 in [1]
5.30.10. Supervised learning for deep networks JD
6.6.11. Deep (convolutional) networks JD
7.13.11. Unsupervised learning, EM algorithm, mixture models BF
8.20.11. Bayesian learning BF
9.27.11. Hidden Markov Models BF
10.4.12. Structured output SVMs VF
11.11.12. Markov Random Fields BF
12.18.12 Ensembling I JD
13.8.1. Ensembling II JD
courses/be4m33ssu/lectures.txt · Last modified: 2018/12/18 08:35 by drchajan