DH-406 / 4 credits

Teacher: 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

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:

  • Choose an appropriate learning algorithm for a given problem
  • Derive the mathematical formulations of basic supervised and unsupervised learning algorithms
  • Develop basic supervised and unsupervised learning models
  • Explain the differences between different machine learning algorithms
  • Assess / Evaluate the advantages and limitations of different machine learning algorithms

Transversal skills

  • Assess progress against the plan, and adapt the plan as appropriate.
  • Continue to work through difficulties or initial failure to find optimal solutions.

Teaching methods

Ex cathedra with exercises, 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

  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for DH
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for DH
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 2 Hour(s) per week x 14 weeks
  • Exam form: Written (winter session)
  • Subject examined: Machine learning for DH
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Practical work: 2 Hour(s) per week x 14 weeks

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     

Monday, 8h - 10h: Lecture BC02

Monday, 10h - 12h: Exercise, TP BC02