Statistical learning theory: различия между версиями
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Bbauwens (обсуждение | вклад) мНет описания правки |
Bbauwens (обсуждение | вклад) мНет описания правки |
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A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book: | |||
Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012. | Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012. | ||
== Office hours == | == Office hours == | ||
Версия от 16:10, 13 сентября 2017
General Information
Course materials
| Date | Summary | Lecture notes | Problem list |
|---|---|---|---|
| 5 sept | PAC-learning and VC-dimension: definitions | 1st and 2nd lecture Updated on 13th of Sept. | Problem list 1 |
| 12 sept | PAC-learning and VC-dimension: proof of fundamental theorem | Problem list 2
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| 19 sept | Sauer's lemma, agnostic PAC-learning, structural risk minimization | ||
| 26 sept | Computational learning theory |
| |
| 3 okt | Boosting: the adaBoost algorithm | ||
| 10 okt | Boosting: several other algorithms | ||
| 17 okt | Online learning algorithms |
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A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book:
Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.
Office hours
| Person | Monday | Tuesday | Wednesday | Thursday | Friday | ||
|---|---|---|---|---|---|---|---|
| Bruno Bauwens | 15:05–18:00 | 15:05–18:00 | Room 620 | ||||
| Quentin Paris |