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Large-scale learning and inference

This is the website for the probabilistic part of TC4 (Master 2 AIC, 2019-2020). It does not contain any information about the first three lessons taught by Jean-Christophe Janodet.

About

This course covers:

  • important probability concepts that are useful for machine learning,
  • the distinction between discriminative and generative modeling,
  • Hidden Markov Models (HMM),
  • parameter inference in HMM,
  • the Viterbi algorithm.

This knowledge will be useful for OPT2 (Graphical Models). Exercises will focus on Natural Language Processing (NLP) problems:

  • part-of-speech tagging,
  • typing errors correction.

This course was originally taught by Alex Allauzen.

Grading Scheme

Contact

You can contact me at caio.corro@u-psud.fr, either in French or English, with a subject starting with [TC4]. Please, do not worry about typos or not being overly formal enough (just treat your instructors and colleagues with the same respect you would like to be treated).