Statistical learning is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome.
This course gives an introduction to this exciting field. In the first part, we will introduce basic techniques such as logistic regression, multilayer perceptrons, nearest neighbor approaches, both from a theoretical and methodological point of views. In the second part, we will focus on more advanced techniques such as kernel methods, which is a versatile tool to represent data, in combination with (un)supervised learning techniques that are agnostic to the type of data that is learned from. The learning techniques that will be covered include regression, classification, clustering and dimension reduction. We will cover both the theoretical underpinnings of kernels, as well as a series of kernels that are important in practical applications. Finally we will touch upon topics of active research, such as large-scale kernel methods and the use of kernel methods to develop theoretical foundations of deep learning models.
The grading of the class will be done with (i) one final exam, (ii) two homeworks.
Addtional exercises can be found in this booklet.
The final exam will take place in January 2025 and will last 2 hours (the location and precise date is to be confirmed). You are allowed to bring one single A4 sheet of handwritten notes.
Lectures are scheduled from 9:45 to 11:15 on Mondays and from 8:15 to 9:45 on Thursdays. The first part of the class will recap basic supervised learning techniques, theory, and algorithms. Lecture notes or slides will be updated here on the fly.
You can download the slides for all lectures of the second part of the class (advanced kernel methods) here! Each lecture corresponds to a range of slides. Slides are frequently updated. Please let us know if you spot typos!
Homework 1.
The first homework is due by Friday, November 22, 2024. The report can be written in English or in French. It is to be uploaded using the form here as a pdf report and a code file (.py or .ipynb). If the upload does not succeed (for some reason), send an email to
but only after you have tried the upload.
# | Date | Room | Teacher | Topic | |
---|---|---|---|---|---|
1 | 23/09/2024 | C008 | PG | Supervised learning basics | |
2 | 26/09/2024 | C008 | PG | Linear regression | |
3 | 30/09/2024 | C008 | MA | Linear regression (solution exercise 2.1) | |
4 | 03/10/2024 | C010 | MA | Logistic regression | |
5 | 07/10/2024 | C003 | PG | Maximum likelihood (notebook) | |
10/10/2024 | No Class | ||||
6 | 14/10/2024 | C003 | PG | K-Nearest Neighbors | |
7 | 17/10/2024 | C010 | PG | Lasso | |
8 | 21/10/2024 | H101 | PG | Multilayer Perceptrons | |
9 | 24/10/2024 | C010 | JM | Positive definite kernels and RKHS, slides 1-30 | |
10 | 04/11/2024 | C009 | JM | RKHSs, slides 31-47 | |
11 | 07/11/2024 | online | JM (online) | Smoothness functional, kernel tricks, Slides 48-61 (1) Try to do the quizz on slide 48 (2) Watch the last 7 minutes of this video. (3) Watch the first 16 minutes of this video. (4) Interactive session will be at 9am on Zoom, id meeting 937 8058 5239 using the password given during the class. |
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12 | 14/11/2024 | C010 | PG | Kernel tricks, Representer Theorem, and KRR, slides 62-92 | |
13 | 18/11/2024 | C010 | PG | KRR, WKRR, KLR, slides 93-111 | |
14 | 21/11/2024 | C010 | MA | ||
15 | 25/11/2024 | C010 | MA | ||
16 | 28/11/2024 | C010 | MA | ||
17 | 02/12/2024 | C010 | JM | ||
18 | 05/12/2024 | C010 | MA | ||
19 | 09/12/2024 | C010 | JM | ||
20 | 12/12/2024 | C010 | JM | ||
21 | 16/12/2024 | C010 | JM | ||
22 | 19/12/2024 | C010 | JM | ||
23 | 06/01/2025 | C010 | MA | 24 | 09/01/2025 | C010 | MA | End/01/2025 |
# | End/01/2025 | Exam |