Machine Learning for Engineers
EE-613
Overall information
In general (except stated otherwise)
- Lecture: 10h15 - 12h00 - Room: DIA005
- Laboratory: 14h15 - 16h00 - Room CO4
- Unless stated otherwise, laboratory feedbacks (whether graded or not) are done the week after the session
- Lab corrections are done in general at the beginning of the afternoon session
- Link to Zoom sessions (URL)
- Course (C) and Labs (L). -- Graded labs are in bol... (Text and media area)
- Announcements (Forum)
- Discussions (Forum)
- Link for the labs on JupyterHub (select image "EPFL, MLFE") (URL)
- How to use JupyterHub (File)
- How to download and run labs locally (File)
1. ML introduction
Lecture: Jean-Marc Odobez
Date: 21/09/2023 (morning)
Laboratory on python and optimization: Olivier Canévet
Date: 21/09/2023 (afternoon)
Release: 21/09/2023
- EE613 - Lecture 1 - Introduction (File)
- EE613 - Recording of introductory course (URL)
- Feedback on labs about Python and optimization (File)
2. Bayesian I
Lecture: Jean-Marc Odobez
Date: 28/09/2023 (morning)
Laboratory (not graded): Olivier Canévet
Released: 28/09/2023 - you can start doing the lab by yourself, to understand the course
Date: 05/10/2023 (morning) - Olivier will be present to answer questions
Feedback: 05/10/2023 (afternoon)
- EE613 - Lecture 2 - Introduction to Graphical Models (File)
- Lecture 2 - Written laboratory (File)
- Lecture Recording (URL)
- Introduction to graphical models - solution slides (File)
3. Bayesian II
Lecture: Jean-Marc Odobez
Date: 28/09/2023 (afternoon)
Laboratory (partly graded) : Olivier Canévet
- Released: 28/09/2023
- Date of lab for answering questions: 05/10/2023 (morning & afternoon)
- Due: 11th of October
Expected date of grades & feedback: 19th of October
- EE613 - Lecture 3 - Learning in Graphical Models - Gaussian Mixture Models (File)
- Lecture recording - Graphical Model 2 - ML/MAP - GMM - EM (URL)
- Solution Recordings - GMM Part 1 and 3 (URL)
- Solution Slides - GMM Part 2 (File)
- Solution Slides - GMM Part 1 and 3 (File)
4. Hidden Markov Models
Lecture: Sylvain Calinon
Date: 12.10.2023 (morning)
Laboratory: Tobias Löw
Date: 12.10.2023 (afternoon)
Release: 12.10.2023
Due: 18.10.2023
5. Dimensionality reduction
Lecture: Jean Marc Odobez
Date: 19/10/2023 (morning)
Laboratory: no laboratory
- EE613 - Lecture 5 - Dimensionality Reduction (File)
- Lecture recording - Dimensionality reduction - Passcode: =%5A@p@d (URL)
6. Decision trees
Lecture: Jean Marc Odobez
Date: 26/10/2023 (morning)
Laboratory: Anshul Gupta
Date: 26/10/2023 (afternoon)
Release:
Due: 9/11/2023 - feedback 23/11/2023
- EE613 - Lecture 6 - Decision Trees - Random Forest (File)
- Decision Tree/Bagging/Random Forest lecture recording - Passcode: &uE4W3q% (URL)
- Decision Trees Lab solutions (File)
7. Linear regression
Lecture: Sylvain Calinon
Date: 02/11/2023 (morning)
Laboratory: Tobias Löw
Date: 02/11/2023 (afternoon)
Release:
Due:
8. Nonlinear regression
Lecture: Sylvain Calinon
Date: 09/11/2023 (morning)
Laboratory: Tobias Löw
Date: 09/11/2023 (afternoon)
Release:
Due:
9. Kernel Methods
Lecture: Jean Marc Odobez
Date: 16/11/2023 (morning)
Laboratory: Anshul Gupta and Samy Tafasca
Date: 16/11/2023 (afternoon)
Release:
Due:
- EE613 - Lecture 9 - Kernels and Support Vector Machines (File)
- Kernel methods - SVM lecture recording (URL)
- Solution Video Recording (URL)
- Solution Slides (File)
10. Tensor factorization
Lecture: Sylvain Calinon
Date: 23/11/2023 (morning)
Laboratory: Tobias Löw
Date: 23/11/2023 (afternoon)
Release:
Due:
11. Deep learning 1
Lecture: Michael Villamizar
Date: 30/11/2023 (morning)
Laboratory: Olivier Canèvet
Date: 30/11/2023 (afternoon)
Release:
Due:
"intro_to_pytorch" (50 points): a quick introduction on how to use the main PyTorch components
"logreg_and_mlp_in_pytorch" (50 points): implementing a multi layer perceptron with PyTorch
- EE613 - Lecture 11 - Deep learning 1 (File)
- Lecture recording - Deep learning 1 (URL)
- EE613 - Feedback on lab on PyTorch and MLP (File)
- lab (Assignment)
12. Deep learning 2
Lecture: Michael Villamizar
Date: 07/12/2023 (morning)
Laboratory: Olivier Canèvet
Date: 07/12/2023 (afternoon)
Release:
Due:
- EE613 - Lecture 12 - Deep learning 2 (File)
- Lecture recording - Deep learning 2 (URL)
- Lab recording : feedback on second deep learning lab (URL)
13. Deep learning 3
Lecture: Michael Villamizar
Date: 14/12/2023 (morning)
Laboratory: Olivier Canèvet
Date: 14/12/2023 (afternoon)
Release:
Due: 20/12/2023
Notes:
Lab: "alexnet_finetuning" (100 points): Finetuning an AlexNet model on a fruit data set to design a smart weigh scale.
- EE613 - Lecture 13 - Deep learning 3 (File)
- EE 613 - Lecture recording (URL)
- Feedback on lab "CNN" (File)
- Feedback on lab "CNN" (URL)
14. Deep learning 4
Lecture: Michael Villamizar
Date: 21/12/2023 (morning)
- EE613 - Lecture 14 - Deep learning 4 (File)
- Lecture recording - Deep learning 4 (URL)
- Case study on brain tumor segmentation (File)
- Feedback on lab "finetuning" (File)