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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|**Martin Jaggi**| |Instructor|**Ruediger Urbanke**| |
| - | |Office|[[http://plan.epfl.ch/?room=INJ339|INJ 339]]| | + | |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 37059**||Phone|**+41 21 69 37692**| |
| - | |Email|**martin.jaggi@epfl.ch**| | + | |Email|**martin.jaggi@epfl.ch**||Email|**ruediger.urbanke@epfl.ch**| |
| - | |Office Hours|**By appointment**| | + | |Office Hours|**By appointment**||Office Hours|**By appointment**| |
| - | | | | | + | |
| - | |Instructor|**Ruediger Urbanke**| | + | |
| - | |Office|[[http://plan.epfl.ch/?room=INR116|INR 116]]| | + | |
| - | |Phone|**+41 21 69 37692**| | + | |
| - | |Email|**ruediger.urbanke@epfl.ch**| | + | |
| - | |Office Hours|**By appointment**| | + | |
| \\ | \\ | ||
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| See the course [[http://edu.epfl.ch/coursebook/en/pattern-classification-and-machine-learning-CS-433|information]]. | See the course [[http://edu.epfl.ch/coursebook/en/pattern-classification-and-machine-learning-CS-433|information]]. | ||
| ==== Special Announcements ==== | ==== Special Announcements ==== | ||
| - | * Practicals: Labs and Projects will be in **Python** this year. | + | * Projects: There will be two //group projects// during the course. |
| - | * 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. | + | * Project 1 counts 10% and is due Oct 31st. |
| - | * 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]]). | + | * 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 ==== | ==== Detailed Schedule ==== | ||
| - | ^ Date ^ Topics Covered ^ Reading Assignment ^ Exercises ^ Solutions ^ | + | (tentative, subject to changes) |
| - | | 20/9| what | what | what | what | | + | ^ Date ^ Topics Covered ^ Exercises ^ Projects ^ |
| - | | 22/9| what | what | what | what | | + | | 20/9 | Introduction | | | |
| - | | 27/9 | what | what | what | what | | + | | 22/9 | Linear Regression | Lab 1 | | |
| - | | 29/9 | what | what | what | what | | + | | 27/9 | Cost Functions | | | |
| - | | 04/10 | what | what | what | what | | + | | 29/9 | Optimization | Lab 2 | | |
| - | | 06/10 | what | what | what | what | | + | | 04/10 | Least Squares, ill-conditioning | | | |
| - | | 11/10 | what | what | what | what | | + | | 06/10 | Maximum Likelihood, Overfitting | Lab 3 | | |
| - | | 13/10 | what | what | what | what | | + | | 11/10 | Cross-Validation | | | |
| - | | 18/10 | what | what | what | what| | + | | 13/10 | Bias-Variance decomposition | Lab 4 | | |
| - | | 20/10 | what | what | what | what| | + | | 18/10 | Classification | | | |
| - | | 25/10 | what | what | what | what | | + | | 20/10 | Logistic Regression | Lab 5 | | |
| - | | 27/10 | what | what | what | what | | + | | 25/10 | Generalized Linear Models | | | |
| - | | 01/11 | what | what | what | what | | + | | 27/10 | k-Nearest Neighbor | Lab 6 | | |
| - | | 03/11 | what | what | what | what | | + | | 01/11 | Support Vector Machines | | Proj. 1 due 31.10. | |
| - | | 08/11 | what | what | what | what | | + | | 03/11 | Kernel Regression | Lab 7 | | |
| - | | 10/11 | what | what | what | what | | + | | 08/11 | Unsupervised Learning | | | |
| - | | 15/11 | what | what | what | what | | + | | 10/11 | k-Means | Lab 8 | | |
| - | | 17/11 | what | what | what | what | | + | | 15/11 | Gaussian Mixture Models | | | |
| - | | 22/11 | what | what | what | what | | + | | 17/11 | EM algorithm | Lab 9 | | |
| - | | 24/11 | what | what | what | what | | + | | 22/11 | Matrix Factorizations | | | |
| - | | 29/11 | what | what | what | what | | + | | 24/11 | Recommender Systems | Lab 10 | | |
| - | | 01/12 | what | what | what | what | | + | | 29/11 | SVD and PCA | | | |
| - | | 06/12 | what | what | what | what | | + | | 01/12 | SVD and PCA | Lab 11 | | |
| - | | 08/12 | what | what | what | what | | + | | 06/12 | Neural Networks | | | |
| - | | 13/12 | what | what | what | what | | + | | 08/12 | Multi-Layer Perceptron | Lab 12 | | |
| - | | 15/12 | what | what | what | what | | + | | 13/12 | Neural Networks, CNNs | | | |
| - | | 20/12 | what | what | what | what | | + | | 15/12 | Decision Trees, Random Forests | Lab 13 | | |
| - | | 22/12 | what | what | what | what | | + | | 20/12 | BayesNet and Belief Propagation | | | |
| - | ==== Textbook ==== | + | | 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// | ||