===== Machine Learning =====

(formerly known as Pattern Classification and Machine Learning)\\
previous year's website: [[http://icapeople.epfl.ch/mekhan/pcml15.html]]


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|Instructor|**Martin Jaggi**|    |Instructor|**Ruediger Urbanke**|
|Office|[[http://plan.epfl.ch/?room=INJ339|INJ 339]]||Office|[[http://plan.epfl.ch/?room=INR116|INR 116]]|
|Phone|**+41 21 69 37059**||Phone|**+41 21 69 37692**|
|Email|**martin.jaggi@epfl.ch**||Email|**ruediger.urbanke@epfl.ch**|
|Office Hours|**By appointment**||Office Hours|**By appointment**|
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|Teaching Assistant |**Mohamad Dia**|Email|**mohamad.dia@epfl.ch**| |Office|[[http://plan.epfl.ch/?room=INR140|INR 140]]|
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|Teaching Assistant |**Ksenia Konyushkova**|Email|**ksenia.konyushkova@epfl.ch**| |Office|[[http://plan.epfl.ch/?room=BC304|BC304]]|
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|Teaching Assistant |**Victor Kristof**|Email|**victor.kristof@epfl.ch**| |Office|[[http://plan.epfl.ch/?room=BC204|BC204]]|
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|Teaching Assistant |**Taylor Newton**|Email|**taylor.newton@epfl.ch**| |Office|[[http://plan.epfl.ch/?room=B1%205%20284.045|B1 Geneva]]|
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|Teaching Assistant |**Farnood Salehi**|Email|**farnood.salehi@epfl.ch**| |Office|[[http://plan.epfl.ch/?room=BC250|BC250]]|
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|Teaching Assistant |**Benoît Seguin**|Email|**benoit.seguin@epfl.ch**| |Office|[[http://plan.epfl.ch/?room=INN140|INN 140]]|
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|Student Assistant |**Frederik Kunstner**|Email|**frederik.kunstner@epfl.ch**|
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|Student Assistant |**Fayez Lahoud**|Email|**fayez.lahoud@epfl.ch**|
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|Student Assistant |**Tao Lin**|Email|**tao.lin@epfl.ch**|
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|Student Assistant |**Arnaud Miribel**|Email|**arnaud.miribel@epfl.ch**|
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|Student Assistant |**Vidit Vidit**|Email|**vidit.vidit@epfl.ch**|
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|Lectures|**Tuesday** |**8:15 - 10:00**  (Room: [[http://plan.epfl.ch/?lang=en&room=CE%201|CE1]])|
|        |**Thursday** |**8:15 - 10:00**  (Room: [[http://plan.epfl.ch/?lang=en&room=CE%204|CE4]])|
|Exercises|**Thursday** |**14:15 - 16:00**  (Room: [[http://plan.epfl.ch/?lang=en&room=INF%20119|INF119]],[[http://plan.epfl.ch/?lang=en&room=INJ218|INJ218]],[[http://plan.epfl.ch/?lang=en&room=INM11|INM11]],[[http://plan.epfl.ch/?lang=en&room=INM202|INM202]])|
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|**Language**:|  |English|
|**Credits **:|  |7 ECTS|
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See the course [[http://edu.epfl.ch/coursebook/en/pattern-classification-and-machine-learning-CS-433|information]].
==== Special Announcements ====
  * 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. All Labs and Projects will be in //Python// this year. See Lab 1 to get started.
  * Labs: Weekly in the following rooms: INF119 (A-E); INJ218 (F-M); INM11 (N-Q); INM202 (R-Z)
    * Code Repository: [[https://github.com/epfml/ML_course|github.com/epfml/ML_course]]
  * Lectures: //Clicker//: For some active participation in the lectures, please point your browser to this [[http://seance.epfl.ch/ng/room/57d862f5c301cef78a5b769a|speak-up room]]
    * Lecture notes: We provide PDF //lecture notes// here below and also on [[http://nb.mit.edu/subscribe?key=GVKmwZM2Dnhi4TBF7CK7hwf9joksteycFDDSNHnNsOkNOK1BZq|Nota Bene]] so you can comment & discuss them.

==== Detailed Schedule ====
(tentative, subject to changes)
^ Date ^ Topics Covered ^ Exercises  ^ Projects ^
| 20/9 | Introduction | | |
| 22/9 | Linear Regression | Lab 1 | |
| 27/9 | Cost Functions | | |
| 29/9 | Optimization | Lab 2 | |
| 04/10 | Least Squares, ill-conditioning | | |
| 06/10 | Maximum Likelihood, Overfitting | Lab 3 | |
| 11/10 | Cross-Validation | | |
| 13/10 | Bias-Variance decomposition | Lab 4 | |
| 18/10 | Classification | | |
| 20/10 | Logistic Regression | Lab 5 | |
| 25/10 | Generalized Linear Models | | |
| 27/10 | k-Nearest Neighbor | Lab 6 | |
| 01/11 | Support Vector Machines | | Proj. 1 due 31.10. |
| 03/11 | Kernel Regression | Lab 7 | |
| 08/11 | Unsupervised Learning | | |
| 10/11 | k-Means | Lab 8 | |
| 15/11 | Gaussian Mixture Models | | |
| 17/11 | EM algorithm | Lab 9 | |
| 22/11 | Matrix Factorizations | | |
| 24/11 | Recommender Systems | Lab 10 | |
| 29/11 | SVD and PCA | | |
| 01/12 | SVD and PCA | Lab 11 | |
| 06/12 | Neural Networks | | |
| 08/12 | Multi-Layer Perceptron | Lab 12 | |
| 13/12 | Neural Networks, CNNs | | |
| 15/12 | Decision Trees, Random Forests | Lab 13 | |
| 20/12 | BayesNet and Belief Propagation | | |
| 22/12 | Gaussian Processes | Lab 14 | Project 2 due |

==== Textbook ====
Christopher Bishop, //Pattern Recognition and Machine Learning// \\
Kevin Murphy, //Machine Learning: A Probabilistic Perspective// \\
Shai Shalev-Shwartz, Shai Ben-David, //Understanding Machine Learning//