Machine Learning
(formerly known as Pattern Classification and Machine Learning)
previous year's website: http://icapeople.epfl.ch/mekhan/pcml15.html
Instructor | Martin Jaggi | Instructor | Ruediger Urbanke | |
Office | INJ 339 | Office | INR 116 | |
Phone | +41 21 69 37059 | Phone | +41 21 69 37692 | |
martin.jaggi@epfl.ch | ruediger.urbanke@epfl.ch | |||
Office Hours | By appointment | Office Hours | By appointment |
Teaching Assistant | Mohamad Dia | mohamad.dia@epfl.ch | Office | INR 140 | ||
Teaching Assistant | Ksenia Konyushkova | ksenia.konyushkova@epfl.ch | Office | BC304 | ||
Teaching Assistant | Victor Kristof | victor.kristof@epfl.ch | Office | BC204 | ||
Teaching Assistant | Taylor Newton | taylor.newton@epfl.ch | Office | B1 Geneva | ||
Teaching Assistant | Farnood Salehi | farnood.salehi@epfl.ch | Office | BC250 | ||
Teaching Assistant | BenoƮt Seguin | benoit.seguin@epfl.ch | Office | INN 140 | ||
Student Assistant | Frederik Kunstner | frederik.kunstner@epfl.ch | ||||
Student Assistant | Fayez Lahoud | fayez.lahoud@epfl.ch | ||||
Student Assistant | Tao Lin | tao.lin@epfl.ch | ||||
Student Assistant | Arnaud Miribel | arnaud.miribel@epfl.ch | ||||
Student Assistant | Vidit Vidit | vidit.vidit@epfl.ch |
Lectures | Tuesday | 8:15 - 10:00 (Room: CE1) |
Thursday | 8:15 - 10:00 (Room: CE4) | |
Exercises | Thursday | 14:15 - 16:00 (Room: INF119,INJ218,INM11,INM202) |
Language: | English | |
Credits : | 7 ECTS |
See the course 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: github.com/epfml/ML_course
- Lectures: Clicker: For some active participation in the lectures, please point your browser to this speak-up room
- Lecture notes: We provide PDF lecture notes here below and also on 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