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b191
courses
be4m33ssu
lectures
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Syllabus
Lecture
Date
Topic
Lecturer
Pdf
Notes
1.
24. 9.
Introduction
BF
2.
1. 10.
Empirical risk
VF
, ( printable:
)
ch 2 in [1]
3.
8. 10.
Empirical risk minimization
VF
, ( printable:
)
ch 2 in [1], ch 3 in [1]
4.
15. 10.
Support Vector Machines I
VF
, ( printable:
)
ch 4 in [1], ch 12 in [2]
5.
22. 10.
Support Vector Machines II
VF
, (printable:
)
ch 5 in [1]
6.
29. 10.
Supervised learning for deep networks
JD
7.
5. 11.
Deep (convolutional) networks
JD
,
SGD
8.
12. 11.
Unsupervised learning, EM algorithm, mixture models
BF
9.
19. 11.
Bayesian learning
BF
10.
26. 11.
Hidden Markov Models
BF
11.
3. 12.
Markov Random Fields
BF
for additional reading (not part of the exam)
12.
10. 12.
Ensembling I
JD
13.
17. 12.
Ensembling II
JD
14.
7. 1.
reserve
courses/be4m33ssu/lectures.txt
· Last modified: 2019/12/10 12:14 by
drchajan