Course M2 IASD, Fondamentaux de l’apprentissage automatique

Master 2 students, Universite Paris, 1900

Information for the students:

The room for question-answer sessions: B113

Schedule for the lectures:

  1. Monday 21/09, 13:45-17:00
  2. Wednesday 07/10, 13:45-17:00
  3. Wednesday 4/11, 13:45-17:00
  4. Tuesday 10/11, 13:45-17:00
  5. Tuesday 8/12, 13:45-17:00

Lecture videos for the course :

Hello words (10 mins)

  1. Lecture ‘Introduction and basic concepts’ (Total duration: 4 hours)

    1.1. Introductory words (30 mins)

    1.2. Learning machine learning (22 mins)

    1.3. Data space (22 mins)

    1.4. Distance in data space (42 mins)

    1.5. Mean point (19 mins)

    1.6. Conditional probability (34 mins)

    1.7. Bayesian networks (39 mins)

    1.8. Probability Density Function (35 mins)

  2. Lecture ‘Clustering’ (Total duration 2.5 hours)

    2.1. Introduction of clustering problem (25 mins)

    2.2. K-means clustering algorithm (33 mins)

    2.3. Hierarchical clustering (23 mins)

    2.4. Density- and graph-based clustering (29 mins)

    2.5. Assessment of clustering quality (31 mins)

  3. Lecture ‘Dimensionality reduction’

  4. Lecture ‘Manifold learning’

  5. Lecture ‘Selected topics in unsupervised machine learning’

In case you want to downloads all videos and watch locally, download them here.

Lecture slides can be downloaded here