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The Elements of Statistical learning, PhD course, 2010 |
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The course is a study group on the book ”The elements of statistical learning” by Hastie, Tibshirani and Friedman. Anyone is welcome to attend. We meet once a week and two persons are responsible for one session each by giving a ~30 minute presentation of the material. Everyone else is supposed to read the material. During and after the presentations we have discussions. PhD students that wish to get credits for the course (7.5 hp) have to give two presentations. Welcome! |
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Schedule |
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Day |
Time |
Room |
Content |
Responsible |
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Mon 22 March |
15.15-17.00 |
MVL22 |
2. Overview 3.1-.3.3 Linear Methods for Regression |
Mats Rudemo Leonid Molokov |
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Tue 13 April |
13.15-15.00 |
MVL15 |
3.4-3.9 Linear Methods for Regression 4 Linear Methods for Classification |
Alexandra Jauhiainen Magnus Röding |
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Tue 20 April |
13.15-15.00 |
MVL15 |
7.1-7.8 Model Assessment and selection 7.9-7.12 Model Assessment and selection |
Azam Sheikh Muhammad Leonid Molokov |
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Tue 27 April |
13.15-15.00 |
MVL15 |
14.1-14.3 Unsupervised learning 14.4 –14.10 Unsupervised learning |
Azam Sheikh Muhammad Magnus Röding |
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Tue 4 May |
13.15-15.00 |
MVL15 |
18.1-18.4 High-Dimensional Problems 18.5-18.8 High-Dimensional Problems |
Malin Östensson
Marcus Isaksson |
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Tue 11 May |
13.15-15.00 |
MVL15 |
6 Kernel Smoothing Methods 8 Model Inference and Averaging |
Jenny Jonasson
Alexandra Jauhiainen |
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Tue 18 May |
13.15-15.00 |
MVL15 |
11 Neural Networks 12.1-12.3 Support Vector Machines |
Malin Östensson Marcus Isaksson |
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Tue 25 May |
13.15-15.00 |
MVL15 |
13 Prototype Methods and Nearest Neighbors 5.1-5.5, 5.9 Basis Expansion and Regularization |
Mats Rudemo
Jenny Jonasson |