Coursebooks 2017-2018

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Signal processing and machine learning for DH

DH-406

Lecturer(s) :

Ridolfi Andrea
Salzmann Mathieu

Language:

English

Summary

This course aims to introduce the basic principles of signal processing and machine learning in the context of the digital humanities. Exercises, numerical examples and computer sessions will allow the students to acquire a practical understanding of the techniques studied in class.

Content

Signal Processing

  1. Sampling & quantization: Bringing the data to the digital world.
  2. Noise, features, and models: Beyond good and evil data.
  3. Tools for feature extraction: Cooking the data to make it eatable.

Machine Learning

  1. Supervised regression: Linear models, kernel methods.
  2. Supervised classification: Linear models, kernel methods, deep learning.
  3. Unsupervised learning: Dimensionality reduction, clustering, topic models.

Keywords

Signal processing, sampling and quantization, spectral analysis, feature extraction, machine learning, digital humanities, supervised and unsupervised learning.

Learning Prerequisites

Required courses

Programming, Linear algebra, Calculus, Probability and Statistics (e.g., Probabilities and statistics MATH-232 or Stochastic Models in Communications COM-300).

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Ex cathedra with exercises, numerical examples, computer sessions.

Expected student activities

Attendance at lectures, completing exercises, testing presented methods with a mathematical computing language (Matlab or similar).

Assessment methods

Final exam with both theoretical and practical problems.

Supervision

Office hours Yes
Assistants No
Forum Yes

Resources

Bibliography

Notes/Handbook

Course slides

In the programs

Reference week

 MoTuWeThFr
8-9BC02    
9-10    
10-11BC02    
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
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