Machine Learning CS-433 – 2023

This is the OLD 2023 course website. For the current one, see here.

This course is offered jointly by the TML and MLO groups. Previous year’s website: ML 2022.
See here for the ML4Science projects.

Contact us: Use the discussion forum. You can also email the head assistant Lara Orlandic, and CC both instructors.

Instructors: Nicolas Flammarion and Martin Jaggi

 
Teaching Assistants
  • Maksym Andriushchenko
  • Francesco D’Angelo
  • Corentin Dumery
  • Dongyang Fan
  • Simin Fan
  • Hojjat Karami
  • Atli Kosson
  • Skander Moalla
  • Lara Orlandic
  • Hristo Papazov
  • Aditya Varre
  • Oguz Yüksel
 Student Assistants
  • Erwan Emlil
  • Vinko Sabolcec
  • Mikhail Seliugin
  • Guanyu Zhang
  • Yauheniya Karelskaya
  • Alejandro Hernandez Cano
  • Naisong Zhou
  • Aybars Yazici
  • Philippe Servant
  • Yiyang Feng
  • Leonardo Trentini
  • Simon Halstensen
  • Mathis Randl
  • Mohamed Hadhri
  • Dong Chu
Lectures Tuesday 16:15 – 18:00 in Rolex Learning Center
  Wednesday 10:15 – 12:00 in Rolex Learning Center
Exercises Thursday 14:15 – 16:00

Rooms: INF1, INF119, INJ218, INM202, INR219 
(assignment see course info sheet)
Language:   English
Credits :   8 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

  • Exam Date: Thursday 18.01.2024 from 15h15 to 18h15 (STCC – Garden Full)
  • The links for the exercises signup and the discussion forum. All other materials are here on this page and github.
  • Projects: There will be two group projects during the course.
    • Project 1 counts 10% and is due Oct 30th.
    • Project 2 counts 30% and is due Dec 21st.
  • The videos of the lectures for each week will be available. Labs and projects will be in Python. See Lab 1 to get started.
  • Code Repository for Labs, Projects, Lecture notes: github.com/epfml/ML_course
  • the exam is closed book but you are allowed one crib sheet (A4 size paper, both sides can be used); bring a pen and white eraser; you find the exams from the past years with solutions here:

Detailed Schedule

Lecture notes from each class are made available on github here, and videos here on mediaspace.

Date Topics Covered Lectures Exercises Projects
19/9 Introduction, Linear Regression 01a,01b
01c,01d		    
20/9 Loss functions   Lab 1  
26/9 Optimization 02a    
27/9 Optimization   Lab 2 Project 1 start
03/10 Least Squares, Overfitting 03a,03b    
04/10 Max Likelihood, Ridge Regression, Lasso 03c,03d Lab 3  
10/10 Generalization, Model Selection, and Validation 04a    
11/10 Bias-Variance decomposition 04b Lab 4  
17/10 Classification 05a    
18/10 Logistic Regression 05b Lab 5  
24/10 Support Vector Machines 06a    
25/10 K-Nearest Neighbor 06b Lab 6  
31/10 Kernel Regression 7a   Proj. 1 due 30.10.
01/11 Neural Networks – Basics, Representation Power 7b Lab 7  
07/11 Neural Networks – Backpropagation, Activation Functions 8a   Project 2 start
08/11 Neural Networks – CNNs, Regularization, Data Augmentation, Dropout 8b Lab 8  
14/11 Neural Networks – Transformers 9a    
15/11 Adversarial ML 9b Lab 9  
21/11 Ethics and Fairness in ML 10a, Ethics canvas    
22/11 Unsupervised Learning, K-Means 10b, 10c Lab 10  
28/11 Gaussian Mixture Models 11a    
29/12 EM algorithm 11b Lab 11 & Project Q&A  
05/12 Matrix Factorizations 12a    
06/12 Text Representation Learning 12b Lab 12  
12/12 Self-supervised learning 13a    
13/12 Generative models 13b Lab 13  
19/12 Guest lecture by Devis Tua      
20/12 Projects pitch session (optional)     Proj. 2 due 21.12.

Textbooks

(not mandatory)

Gilbert Strang, Linear Algebra and Learning from Data
Christopher Bishop, Pattern Recognition and Machine Learning
Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning
Michael Nielsen, Neural Networks and Deep Learning

Projects & ML4Science

Projects are done either in ML4Science in collaboration with any lab of EPFL, UniL or other academic institution, or the Reproducibility Challenge for ML papers, or one of the predefined ML challenges.

All info about the interdisciplinary ML4Science projects is available on the separate page here.

Machine Learning CS-433 – 2022

This is the OLD 2022 course website. For the current one, see here.


This course is offered jointly by the TML and MLO groups. Previous year’s website: ML 2023.
See here for the ML4Science projects.

Contact us: Use the discussion forum. You can also email the head assistant Lara Orlandic, and CC both instructors.

Instructors: Nicolas Flammarion and Martin Jaggi

 
Teaching Assistants
  • Maksym Andriushchenko
  • Francesco D’Angelo
  • Corentin Dumery
  • Dongyang Fan
  • Simin Fan
  • Hojjat Karami
  • Atli Kosson
  • Skander Moalla
  • Lara Orlandic
  • Hristo Papazov
  • Aditya Varre
  • Oguz Yüksel
 Student Assistants
  • Erwan Emlil
  • Vinko Sabolcec
  • Mikhail Seliugin
  • Guanyu Zhang
  • Yauheniya Karelskaya
  • Alejandro Hernandez Cano
  • Naisong Zhou
  • Aybars Yazici
  • Philippe Servant
  • Yiyang Feng
  • Leonardo Trentini
  • Simon Halstensen
  • Mathis Randl
  • Mohamed Hadhri
  • Dong Chu
Lectures Tuesday 16:15 – 18:00 in Rolex Learning Center
  Wednesday 10:15 – 12:00 in Rolex Learning Center
Exercises Thursday 14:15 – 16:00

Rooms: INF1, INF119, INJ218, INM202, INR219 
(assignment see course info sheet)
Language:   English
Credits :   8 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

  • Exam Date: Thursday 18.01.2024 from 15h15 to 18h15 (STCC – Garden Full)
  • The links for the exercises signup and the discussion forum. All other materials are here on this page and github.
  • Projects: There will be two group projects during the course.
    • Project 1 counts 10% and is due Oct 30th.
    • Project 2 counts 30% and is due Dec 21st.
  • The videos of the lectures for each week will be available. Labs and projects will be in Python. See Lab 1 to get started.
  • Code Repository for Labs, Projects, Lecture notes: github.com/epfml/ML_course
  • the exam is closed book but you are allowed one crib sheet (A4 size paper, both sides can be used); bring a pen and white eraser; you find the exams from the past years with solutions here:

Detailed Schedule

Lecture notes from each class are made available on github here, and videos here on mediaspace.

Date Topics Covered Lectures Exercises Projects
19/9 Introduction, Linear Regression 01a,01b
01c,01d		    
20/9 Loss functions   Lab 1  
26/9 Optimization 02a    
27/9 Optimization   Lab 2 Project 1 start
03/10 Least Squares, Overfitting 03a,03b    
04/10 Max Likelihood, Ridge Regression, Lasso 03c,03d Lab 3  
10/10 Generalization, Model Selection, and Validation 04a    
11/10 Bias-Variance decomposition 04b Lab 4  
17/10 Classification 05a    
18/10 Logistic Regression 05b Lab 5  
24/10 Support Vector Machines 06a    
25/10 K-Nearest Neighbor 06b Lab 6  
31/10 Kernel Regression 7a   Proj. 1 due 30.10.
01/11 Neural Networks – Basics, Representation Power 7b Lab 7  
07/11 Neural Networks – Backpropagation, Activation Functions 8a   Project 2 start
08/11 Neural Networks – CNNs, Regularization, Data Augmentation, Dropout 8b Lab 8  
14/11 Neural Networks – Transformers 9a    
15/11 Adversarial ML 9b Lab 9  
21/11 Ethics and Fairness in ML 10a, Ethics canvas    
22/11 Unsupervised Learning, K-Means 10b, 10c Lab 10  
28/11 Gaussian Mixture Models 11a    
29/12 EM algorithm 11b Lab 11 & Project Q&A  
05/12 Matrix Factorizations 12a    
06/12 Text Representation Learning 12b Lab 12  
12/12 Self-supervised learning 13a    
13/12 Generative models 13b Lab 13  
19/12 Guest lecture by Devis Tua      
20/12 Projects pitch session (optional)     Proj. 2 due 21.12.

Textbooks

(not mandatory)

Gilbert Strang, Linear Algebra and Learning from Data
Christopher Bishop, Pattern Recognition and Machine Learning
Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning
Michael Nielsen, Neural Networks and Deep Learning

Projects & ML4Science

Projects are done either in ML4Science in collaboration with any lab of EPFL, UniL or other academic institution, or the Reproducibility Challenge for ML papers, or one of the predefined ML challenges.

All info about the interdisciplinary ML4Science projects is available on the separate page here.