Statistical learning theory
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Версия от 19:06, 13 сентября 2017; Bbauwens (обсуждение | вклад)
General Information
Course materials
Date | Summary | Lecture notes | Problem list |
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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 |
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3 okt | Boosting: the adaBoost algorithm | ||
10 okt | Boosting: several other algorithms | ||
17 okt | Online learning algorithms |
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The materials of the first 3 lectures are covered in chapters 1-6 of the following book:
Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.
This book gives a gentle introduction and repeats all necessary background from probability theory and statistics.
Office hours
Person | Monday | Tuesday | Wednesday | Thursday | Friday | ||
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Bruno Bauwens | 15:05–18:00 | 15:05–18:00 | Room 620 | |||
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Quentin Paris |