This is the OLD 2017 course website. For the current one, see here.
This course is offered jointly with the Information Processing Group. (Course formerly known as Pattern Classification and Machine Learning).
Previous year’s website: http://www.epfl.ch/labs/mlo/page-136795.html
Our contact email: epfmlcourse@gmail.com
Instructor | Martin Jaggi | Instructor | Ruediger Urbanke | |
Office | INJ 341 | 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 | Barbier Jean | jean.barbier@epfl.ch | Office | INR139 | |
Teaching Assistant | Karimireddy Sai Praneeth | sai.karimireddy@epfl.ch | Office | INJ339 | |
Teaching Assistant | Liu Wei | wei.liu@epfl.ch | Office | INR038 | |
Teaching Assistant | Lu Jun | jun.lu@epfl.ch | |||
Teaching Assistant | Maksai Andrii | andrii.maksai@epfl.ch | Office | BC307 | |
Teaching Assistant | Zhou Ruofan | ruofan.zhou@epfl.ch | Office | BC366 | |
Student Assistant | Ajalloeian Ahmad | ahmad.ajalloeian@epfl.ch | |||
Student Assistant | Benyahia Yassine | yassine.benyahia@epfl.ch | |||
Student Assistant | Borgeaud dit Avocat William | william.borgeaud@epfl.ch | |||
Student Assistant | Castellon Arevalo Joel | joel.castellonarevalo@epfl.ch | |||
Student Assistant | Champenois Bertrand | bertrand.champenois@epfl.ch | |||
Student Assistant | Charollais Clément | clement.charollais@epfl.ch | |||
Student Assistant | Gucevska Natalija | natalija.gucevska@epfl.ch | |||
Student Assistant | Kunstner Frederik | frederik.kunstner@epfl.ch | |||
Student Assistant | Lahoud Fayez | fayez.lahoud@epfl.ch | |||
Student Assistant | Moreau Hugo | hugo.moreau@epfl.ch |
Lectures | Tuesday | 17:15 – 19:00 Room: CO1 |
Thursday | 16:15 – 18:00 Room: SG1 | |
Exercises | Thursday | 14:15 – 16:00 Room: INF119,INJ218,INM11,INM202 |
Language: | English | |
Credits : | 7 ECTS |
For a summary of the logistics of this course, see the course info sheet here (PDF).
(and also here is a link to official coursebook information).
Special Announcements
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The new exercise sheet, as well as the solution (code only) for last weeks lab session will typically be made available each tuesday (here and on github).
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Projects: There will be two group projects during the course.
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Project 1 counts 10% and is due Oct 30st.
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Project 2 counts 30% and is due Dec 21nd.
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Labs and projects will be in Python. See Lab 1 to get started.
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Code Repository for Labs, Projects, Lecture notes: github.com/epfml/ML_course
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Lectures: Clicker: For some active participation in the lectures, please point your browser to this speak-up room
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The FINAL EXAM is scheduled for January 17th 2018, from 16:15-19:15 in STCC08328 ; the exam is closed book but you are allowed one crib sheet (A4 size paper, both sides can be used), either handwritten or 11 point minimum font size; bring a pen (you cannot use pencils for the MC part due to technical reasons); the exam will have 20+ MCQs (multiple possible answers and negative poins for wrong answers) and 4 regular questions; this year the MCQ part will be graded automatically, so please follow the instructions carefully in how to mark your answers; you find the exam from last year with solutions posted a few lines above;
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Solution to final exam.
Detailed Schedule
All 2017 materials are available on github here (use this instead of the individual links below, or check the 2018 website)
Annotated lecture notes from each class are made available on github here.
Date | Topics Covered | Lectures | Exercises | Projects |
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19/9 | Introduction | 01a,01b | ||
21/9 | Linear Regression, Cost functions | 01c,01d | Lab 1 | |
26/9 | Optimization | 02a | ||
28/9 | Optimization | Lab 2 | ||
03/10 | Least Squares, ill-conditioning, Max Likelihood | 03a,03b | Project 1 details | |
05/10 | Overfitting, Ridge Regression, Lasso | 03c,03d | Lab 3 | |
10/10 | Cross-Validation | 04a | ||
12/10 | Bias-Variance decomposition | 04b | Lab 4 | |
17/10 | Classification | 05a | ||
19/10 | Logistic Regression | 05b | Lab 5 | |
24/10 | Generalized Linear Models | 06a | ||
26/10 | K-Nearest Neighbor | 06b | Q&A for proj. | |
31/10 | Support Vector Machines | 07a | Proj. 1 due 30.10. | |
02/11 | Kernel Regression | 07b | Lab 7 | |
07/11 | Unsupervised Learning, K-Means | 08a,08b | Project 2 details | |
09/11 | K-Means, Gaussian Mixture Models | 08c | Lab 8 | |
14/11 | Mock Exam | |||
16/11 | Gaussian Mixture Models, EM algorithm | 09a | Mock exam | Solutions |
21/11 | Matrix Factorizations | 10a | ||
23/11 | Text Representation Learning | 10b | Lab 10 | |
28/11 | SVD and PCA | 11a | ||
30/11 | SVD and PCA and Neural Networks – Basics | 12a | Lab 11 | |
05/12 | Neural Networks – Representation Power | 12b | ||
07/12 | Neural Networks – Backpropagation, Activation Functions | 12c,12d | Q&A for proj. | |
12/12 | Neural Networks – CNN, Regularization, Data Augmentation, Dropout | 13a,13b | ||
14/12 | Graphical Models – Bayes Nets | 13c | Lab 13 | |
19/12 | Graphical Models – Factor Graphs | 14a,14b | ||
21/12 | Graphical Models – Inference and Sum-Product Algorithm | Proj. 2 due 21.12. |
Textbooks
(not mandatory)
Christopher Bishop, Pattern Recognition and Machine Learning
Kevin Murphy, Machine Learning: A Probabilistic Perspective
Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning
Michael Nielsen, Neural Networks and Deep Learning