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b201
courses
be4m33ssu
lectures
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Syllabus
Lecture
Date
Topic
Lecturer
Pdf
Notes
1.
22. 9.
Introduction
BF
2.
29. 9.
Empirical risk
VF
(printable
)
[1] Chap 2, [2] Chap 7
3.
6. 10.
Empirical risk minimization
VF
(printable
)
[1] Chap 2, [2] Chap 7
4.
13. 10.
Support Vector Machines I
VF
(printable
)
[1] Chap 4, [2] Chap 12
5.
20. 10.
Support Vector Machines II
VF
(printable
)
[1] Chap 5, [2] Chap 12
6.
27. 10.
Supervised learning for deep networks
JD
7.
3. 11.
SGD, Deep (convolutional) networks
JD
8.
10. 11.
Generative learning, EM algorithm
BF
9.
17. 11.
National holiday
10.
24. 12.
Bayesian learning
BF
11.
1. 12.
Hidden Markov Models
BF
12.
8. 12.
Markov Random Fields
BF
13.
15. 12.
Ensembling I
JD
14.
5. 1.
Ensembling II
JD
courses/be4m33ssu/lectures.txt
· Last modified: 2020/12/15 11:56 by
drchajan