| Instructor | Nicolas Macris | Instructor | Ruediger Urbanke |
| Office | INR 134 | Office | INR 116 |
| [email protected] | [email protected] | ||
| Teaching Assistant |
Farzad Pourkamali |
Office | |
| Teaching Assistant |
|
Office | |
| Lectures | Monday, 08:15 – 10:00 | Room | INM 202 |
| Exercises | Tuesday, 17:15 – 19:00 | Room | INR 119 |
Language: English Credits: 4 ECTS Prerequisites:
- Analysis I, II, III
- Linear Algebra
- Machine learning
- Probability
- Algorithms (CS-250)
Here is a link to official coursebook information. Homework: Some homework will be graded. Grading: your grade will be determined based on the following weighted average: 10 % for the Homework, 90 % for the Final Exam. For the graded homeworks, you can discuss the homework with other people. But you have to write down your own solution and note on the first page the set of people that you discussed with.
Special Announcements
Lectures:
Lectures will be in presence on Mondays, from 8:15pm to 10:15pm.
Exercise sessions:
Exercise sessions take place on Tuesdays, from 5pm to 7pm in presence.
Graded homework
The graded homework are collected via this Moodle page. You can either write them by hand and scan or you can use latex: Latex template graded homework. If you cannot compile LaTeX on your own computer, EPFL is providing Overleaf Professional accounts for all students: Overleaf EPFL . With Overleaf you can write and compile LaTeX directly from your web browser. To use the provided template (.tex), you can create a new project and upload the .tex file.
Final exam
The final exam is a 3 hour open-book on-campus exam (lecture notes, exercices, course material, but no electronic devices), held during the regular exam period. This exam will contribute 90% to the grade.
Topics
- PAC learning model (based on Chapters 2-7 in Understanding Machine Learning (UML) by Shalev-Shwartz and Ben David)
- Gradient descent (UML and notes by A. Montanari)
- Tensor decomposition (based on the review on Tensors Decompositions by Rabanser, Shchur and Günnemann). For more advanced material see also: Algorithmic Aspects of Machine Learning by Moitra
Detailed Schedule
See the Moodle page for weekly program and handouts
Textbooks and notes:
- Understanding Machine Learning by Shalev-Shwartz and Ben David
- Bayesian Reasoning and Machine Learning by David Barber(Cambridge)
- Pattern recognition and Machine Learning by Christopher Bishop (Springer)
- Introduction to Tensor Decompositions and their Applications in Machine Learning (Ranbaser, Shchur, Gunneman)
- Probability on Graphs. Random processes on graph and lattices by Geoffrey Grimmett (Cambridge) [Chap 7]
- Neural Tangent Kernel references: