ML 2

Machine learning 2

Description: This course complements Machine Learning 1 with notions of data processing (dimension reduction, etc.), unsupervised learning, active and semi-supervised learning, explicability issues.

Learning outcomes: By the end of this course, students will have completed their breadth approach to machine learning.

Evaluation methods: 2h written test, can be retaken.

Evaluated skills:

• Analyze, design, and build complex systems with scientific, technological, human, and economic components
• Develop in-depth skills in an engineering field and a family of professions

Course supervisor: Arthur Hoarau

Geode ID: 3MD4010