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.
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.
- 50%: Exam on the first part of the course (Jean-Christophe Janodet)
- 50%: Project on the probabilistic part
You can contact me at firstname.lastname@example.org, 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).