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.

Program:

1. • Lectures: 36 hours (9 lectures of 4 hours)
   

2. • Labs: 20 hours (5 labs of 4 hours)
   

3. • Exam form: project + presentation
   

4. • Room: ELD 120
   

Required prior knowledge:

Linear algebra, Probabilities and Statistics, Signal Processing

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 0: Overview -- Feb 20 2015
   
   

2. • Lecture 1: Introduction -- Feb 20 2015
   
   

3. • Lecture 2: Reproducible Research with Python and Bob -- Feb 27 2015
   
   

4. • Lecture 3: Linear Regression -- Mar 6 2015
   
   

5. • Lecture 4: Logistic Regression -- Mar 13 2015
   
   

6. • Lab 1: Linear and Logistic Regression -- Mar 20 2015
   
   

7. • Lecture 5: Artificial Neural Networks -- Mar 27 2015
   
   

8. • Lab 2: Artificial Neural Networks -- Apr 10 2015
   
   

9. • Lecture 6: Dimensionality Reduction and Clustering -- Apr 17 2015
   
   

10. • Lab 3: Dimensionality Reduction and Clustering -- Apr 24 2015
    
    

11. • Lecture 7: Probability Distribution Modelling (1/2) -- May 1 2015
    
    

12. • Lecture 8: Probability Distribution Modelling (2/2) -- May 8 2015
    
    

13. • Lab 4: Probability Distribution Modelling -- May 15 2015
    
    

14. • Lecture 9: Support Vector Machines -- May 22 2015
    
    

15. • Lab 5: Support Vector Machines -- May 29 2015
    

1. • Exam: Homework project given on May 29 2015 to complete (short report + source code) by June 10 (11:59pm) and to present on June 12.