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: 32 hours (8 lectures)
   

2. • Labs: 24 hours (5 labs)
   

3. • Exam form: labs preparation (50%) and final homework project (50%)
   

4. • Room: CO 121
   

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 23 2017
   
   

2. • Lecture 2: Reproducible Research with Python -- Mar 2 2017
   
   

3. • Lecture 3: Logistic Regression -- Mar 9 2017
   
   

4. • Lab 1: Linear and Logistic Regression -- Mar 16 2017
   
   

5. • Lecture 4: Artificial Neural Networks -- Mar 23 2017
   
   

6. • Lab 2: Artificial Neural Networks -- Mar 30 2017
   
   

7. • Lecture 5: Dimensionality Reduction and Clustering -- Apr 6 2017
   
   

8. • Lab 3: Dimensionality Reduction and Clustering -- Apr 13 2017
   
   

9. • Lecture 6: Probability Distribution Modelling (1/2) -- Apr 27 2017
   
   

10. • Lecture 7: Probability Distribution Modelling (2/2) -- May 4 2017
    
    

11. • Lab 4: Probability Distribution Modelling -- May 11 2017
    
    

12. • Lecture 8: Support Vector Machines -- May 18 2017
    
    

13. • Lab 5: Support Vector Machines -- June 1 2017
    

1. • Exam: Homework project given on June 1 2017 to complete (short report + source code) by June 14 (11:59pm) and to present around June 19-21 (to be confirmed).