This is an introductory course that will give you the ability to learn ML autonomously.

Learning goals:
  • apply probability theory to the description of ML algorithms
  • describe the various frameworks of algorithmic learning (exact, PAC, agnostic)
  • analyze the bias-variance tradeoff in light of the information bottleneck principle and double-descent phenomena
  • explain how unsupervised, supervised and reinforcement learning algorithms work

Webpage for the DATAAI922 course on Big Data Processing by Louis Jachiet.

Introduction - Reminder on bases on logics (syntax, semantics...) and overview of several logics (propositional, first order, modal...)