Coursebooks 2017-2018

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Data analysis and model classification

EE-516

Lecturer(s) :

Chavarriaga Lozano Ricardo
Millán Ruiz José del Rocío

Language:

English

Summary

This course introduces several machine learning techniques for the data analysis and classification in Bioengineering applications. Following an application-oriented approach, each technique is illustrated with examples from fields such as neural engineering, movement analysis and bioinformatics.

Content

1 Introduction to Machine learning
Supervised vs Unsupervised approach, Training and testing techniques

 

2 Regression methods
Linear methods, Other methods, Statistical models

 

3 Feature selection
Filters, wrappers, Information theory

 

4 Dimensionality reduction
Principal component analysis (PCA); Independent component analysis (ICA), Clustering approaches

 

5 Temporal pattern recognition / Sequence analysis
Hidden Markov Models

 

6 Case studies - Prosthetics
Application specific constraints (e.g. single trial, compliance, time lag), Wearable robots, Neuroprosthetics

Learning Prerequisites

Important concepts to start the course

Matlab programming (tutorial provided at the beginning of the course)

Teaching methods

Lectures, exercises

Expected student activities

Students will have to carry out weekly exercises and provide a written report.

Assessment methods

Written exam. Final grade: 2/3 Exam, 1/3 Exercises.

Resources

Bibliography

The course has no textbook (it is based on several sources). Suggested reading material will be provided periodically. 

 

The following books are suggested:

- C. Bishop: Neural Networks for Pattern Recognition

- R.O. Duda, P.E. Hart and D.G. Stork: Pattern Classification

- C. Bishop: Pattern Recognition and Machine Learning

Ressources en bibliothèque
Moodle Link

In the programs

Reference week

 MoTuWeThFr
8-9AAC137    
9-10    
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18INF1    
18-19    
19-20    
20-21    
21-22    
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German