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--- en:courses:2016-2017:ml2016 [2016/09/08 03:07]
jaggim
+++ en:courses:2016-2017:ml2016 [2016/09/13 12:08]
ruediger
@@ Line 54: / Line 54: @@
   * 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.
+  * 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]]).
 ==== 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//