Statistical learning theory — различия между версиями
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Bbauwens (обсуждение | вклад) м |
Bbauwens (обсуждение | вклад) м |
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[https://www.dropbox.com/s/r5u7gl33berpokv/syllabusStatisticalLearning.pdf?dl=0 Syllabus for the 1st module] | [https://www.dropbox.com/s/r5u7gl33berpokv/syllabusStatisticalLearning.pdf?dl=0 Syllabus for the 1st module] | ||
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| + | == Homework == | ||
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| + | Will be placed here on Sat 30/09. | ||
| + | The deadline for submission is Thursday 12th of Okt. | ||
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| + | == Project == | ||
| + | |||
| + | Will be placed here. | ||
| + | The deadline for submission is Saturday 21th of Okt. | ||
Версия 16:54, 28 сентября 2017
General Information
Homework
Will be placed here on Sat 30/09. The deadline for submission is Thursday 12th of Okt.
Project
Will be placed here. The deadline for submission is Saturday 21th of Okt.
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 | |
| 19 sept | Sauer's lemma, neural networks and agnostic PAC-learning | 3th lecture Updated on the 23th of Sept. | Problem list 3 |
| 26 sept | Agnostic PAC-learning and 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 | ||
|---|---|---|---|---|---|---|---|
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Bruno Bauwens | 15:05–18:00 | 15:05–18:00 | Room 620 | |||
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Quentin Paris |
