Machine learning for DH
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:
- Linear regression and classification
- Kernel methods
- Deep learning
Unsupervised learning:
- Dimensionality reduction
- Clustering
- 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:
- 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 | Yes |
| Forum | Yes |
Resources
Virtual desktop infrastructure (VDI)
No
Bibliography
Christopher M. Bishop, Pattern Recognition and Machine Learning
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective
Ressources en bibliothèque
- Christopher M. Bishop, Pattern Recognition and Machine Learning
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective
Moodle Link
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
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Machine learning for DH
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Exam form: Written (winter session)
- Subject examined: Machine learning for DH
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Exam form: Written (winter session)
- Subject examined: Machine learning for DH
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional