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en:courses:2016-2017:ml2016 [2016/05/30 14:11]
jaggim
en:courses:2016-2017:ml2016 [2016/09/13 22:49]
jaggim adding clicker room link and notabene link
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-==== Machine Learning ====+===== Machine Learning ​=====
  
-(a.k.a. ​Pattern Classification and Machine Learning)\\+(formerly known as Pattern Classification and Machine Learning)\\
 previous year's website: [[http://​icapeople.epfl.ch/​mekhan/​pcml15.html]] previous year's website: [[http://​icapeople.epfl.ch/​mekhan/​pcml15.html]]
  
  
 +\\ 
 +|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**|
 +\\
 +| | | |
 +|Teaching Assistant |**Mohamad Dia**|Email|**mohamad.dia@epfl.ch**| |Office|[[http://​plan.epfl.ch/?​room=INR140|INR 140]]|
 +| | |
 +|Teaching Assistant |**Ksenia Konyushkova**|Email|**ksenia.konyushkova@epfl.ch**| |Office|[[http://​plan.epfl.ch/?​room=BC304|BC304]]|
 +| | |
 +|Teaching Assistant |**Victor Kristof**|Email|**victor.kristof@epfl.ch**| |Office|[[http://​plan.epfl.ch/?​room=BC204|BC204]]|
 +| | |
 +|Teaching Assistant |**Taylor Newton**|Email|**taylor.newton@epfl.ch**| |Office|[[http://​plan.epfl.ch/?​room=B1%205%20284.045|B1 Geneva]]|
 +| | |
 +|Teaching Assistant |**Farnood Salehi**|Email|**farnood.salehi@epfl.ch**| |Office|[[http://​plan.epfl.ch/?​room=BC250|BC250]]|
 +| | |
 +|Teaching Assistant |**BenoĆ®t Seguin**|Email|**benoit.seguin@epfl.ch**| |Office|[[http://​plan.epfl.ch/?​room=INN140|INN 140]]|
 +| | |
 +|Student Assistant |**Frederik Kunstner**|Email|**frederik.kunstner@epfl.ch**|
 +| | |
 +|Student Assistant |**Fayez Lahoud**|Email|**fayez.lahoud@epfl.ch**|
 +| | |
 +|Student Assistant |**Tao Lin**|Email|**tao.lin@epfl.ch**|
 +| | |
 +|Student Assistant |**Arnaud Miribel**|Email|**arnaud.miribel@epfl.ch**|
 +| | |
 +|Student Assistant |**Vidit Vidit**|Email|**vidit.vidit@epfl.ch**|
 +\\
 +
 +|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]])|
 +\\
 +|**Language**:​| ​ |English|
 +|**Credits **:|  |7 ECTS|
 +\\ 
 +
 +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//

Last modified:: %2016/%09/%13 %22:%Sep