Coursebooks

Machine learning for DH

DH-406

Lecturer(s) :

Salzmann Mathieu

Language:

English

Summary

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.

Content

Supervised learning:

  1. Linear regression and classification
  2. Kernel methods
  3. Deep learning

Unsupervised learning: 

  1. Dimensionality reduction
  2. Clustering
  3. Topic models

 

Keywords

Machine learning, digital humanities, supervised and unsupervised learning

Learning Prerequisites

Required courses

Programming (python), Linear algebra, Probability and Statistics

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

Attend the lectures, complete the exercises, implement and test the studied methods using python

Assessment methods

Final exam with both theoretical and practical problems

Supervision

Office hours No
Assistants No
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Max Welling, A First Encounter with Machine Learning, https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf

Christopher M. Bishop, Pattern Recognition and Machine Learning

Kevin P. Murphy, Machine Learning: A Probabilistic Perspective

Ressources en bibliothèque

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

legend

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