This course (EE-612) presents fundamental tools used in statistical pattern recognition ranging from the most basic (LR, PCA, LDA, MLP, GMM, HMM, SVM). This course could serve as a pre-requisite for more advanced course on Machine Learning.

The main instructors are Sébastien Marcel and André Anjos.

Feedback from students:

“Well organized, good selection of content ... So far the best PhD. course I attended”

“The course is very relevant, dynamic and well conducted”

“La partie théorique est très bien construite et m'a aidée a combler de nombreuses lacunes."

“This course deals with state of the art techniques for pattern recognition. The presentation is particularly clear and precise.”

“The classes were really well organized between theory and labs activities. This kind of organization helps to improve the understanding the intuitions and the algorithms involved in pattern recognition. In special, the lab classes were really helpful. The content was very well documented and this helps a lot for the understanding.”

“I would sincerely like to thank the instructors of this course. The content was explained clearly throughout each lecture and the slides were absolutely neat. Thank you for all your efforts, besides the preparation and presentation of the lectures also for the homeworks and your rapid, clear responses to each of our questions, during class and via e-mail. I think this is precisely how a course should be.”

Program:

1. • Lectures: 8 lectures (every Thu from 9:15 to 12:00 -- exceptionally to 13:00”
   

2. • Labs: 5 labs (python-based to prepare beforehand)
   

3. • Exam form: labs preparation (30%) and final homework project (70%)
   

4. • Room: EPFL MXG110
   

Required prior knowledge:

Linear algebra, Probabilities and Statistics, Signal Processing, Python coding

About the slides and the labs:

In general the slides of a lecture are available after the lecture (later in the day), while the lab material is available before the lecture (early morning).

Content:

1. • Lecture 1: Introduction + Linear Regression -- Feb 21 2019
   
   

2. • Lecture 2: Logistic Regression -- Feb 28 2019
   
   

3. • Lecture 3: Reproducible Research with Python -- Mar 7 2019
   
   

4. • Lab 1: Linear and Logistic Regression -- Mar 14 2019
   
   

5. • Lecture 4: Artificial and Deep Neural Networks -- Mar 21 2019
   
   

6. • Lab 2: Artificial Neural Networks -- Mar 28 2019
   
   

7. • Lecture 5: Dimensionality Reduction and Clustering -- Apr 4 2019
   
   

8. • Lab 3: Dimensionality Reduction and Clustering -- Apr 11 2019
   
   

9. • Lecture 6: Probability Distribution Modelling (1/2) -- Apr 18 2019
   
   

10. • Lecture 7: Probability Distribution Modelling (2/2) -- May 2 2019
    
    

11. • Lab 4: Probability Distribution Modelling -- May 9 2019
    
    

12. • Lecture 8: Support Vector Machines -- May 16 2019
    
    

13. • Lab 5: Support Vector Machines -- May 23 2019
    

1. • Exam: Homework project in June to complete (short report + source code) and to present.