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:

  • Research and Development
  • Development

Course supervisor: Arthur Hoarau

Geode ID: 3MD4010

CM:

  1. Bagging (1.5 h)
  2. Boosting (1.5 h)
  3. Détection anomalies (1.5 h)
  4. Quantification Incertitude (1.5 h)
  5. Apprentissage semi-supervisé (1.5 h)
  6. Explicabilité (1.5 h)

TP:

  1. Bagging (3.0 h)
  2. Forêts aléatoires (3.0 h)
  3. Détection anomalies/OOD (3.0 h)
  4. semi-supervisé/XAI (3.0 h)