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en:courses:2016-2017:ml2016 [2016/09/12 01:06] jaggim schedule |
en:courses:2016-2017:ml2016 [2016/09/13 12:08] ruediger |
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* Practicals: Labs and Projects will be in **Python** this year. | * Practicals: Labs and Projects will be in **Python** this year. | ||
* Projects: There will be two group projects during the course. Project 1 counts 10% and is due Oct 31st. Project 2 counts 30% and is due Dec 22nd. | * Projects: There will be two group projects during the course. Project 1 counts 10% and is due Oct 31st. Project 2 counts 30% and is due Dec 22nd. | ||
+ | * For exercises please go to the following rooms: INF119 (A-E); INJ218 (F-M); INM11 (N-Q); INM202 (R-Z) | ||
* Please make sure that you have registered for the course on [[http://is-academia.epfl.ch/|IS-Academia]]. We will provide the PDF lecture notes here and also on [[http://nb.mit.edu|Nota Bene]] so you can comment & discuss them (see [[http://vimeo.com/7370219|here]]). | * Please make sure that you have registered for the course on [[http://is-academia.epfl.ch/|IS-Academia]]. We will provide the PDF lecture notes here and also on [[http://nb.mit.edu|Nota Bene]] so you can comment & discuss them (see [[http://vimeo.com/7370219|here]]). | ||
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| 25/10 | Generalized Linear Models | | | | | 25/10 | Generalized Linear Models | | | | ||
| 27/10 | k-Nearest Neighbor | Lab 6 | | | | 27/10 | k-Nearest Neighbor | Lab 6 | | | ||
- | | 01/11 | Support Vector Machines | | | | + | | 01/11 | Support Vector Machines | | Proj. 1 due 31.10. | |
| 03/11 | Kernel Regression | Lab 7 | | | | 03/11 | Kernel Regression | Lab 7 | | | ||
| 08/11 | Unsupervised Learning | | | | | 08/11 | Unsupervised Learning | | | | ||
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| 15/12 | Decision Trees, Random Forests | Lab 13 | | | | 15/12 | Decision Trees, Random Forests | Lab 13 | | | ||
| 20/12 | BayesNet and Belief Propagation | | | | | 20/12 | BayesNet and Belief Propagation | | | | ||
- | | 22/12 | Gaussian Processes | Lab 14 | | | + | | 22/12 | Gaussian Processes | Lab 14 | Project 2 due | |
==== Textbook ==== | ==== Textbook ==== | ||
+ | Christopher Bishop, //Pattern Recognition and Machine Learning// \\ | ||
+ | Kevin Murphy, //Machine Learning: A Probabilistic Perspective// \\ | ||
+ | Shai Shalev-Shwartz, Shai Ben-David, //Understanding Machine Learning// |