Course Overview

A prior algorithms (Frequent item sets) & Page Rank, Monte-carlo, MCMC methods: Metropolis-Hastings and Gibbs Sampling, Matrix Factorization (Stochastic Gradient Descent, SVD), Generalized kmeans and its variants (Bach, Online, large scale), Kernel clustering (Support Vector Clustering), Spectral clustering, Classification and Regression Trees, Support Vector regression,Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…Alignment and matching algorithms (local/global, pairwise/multiple), dynamic programming, Hungarian algorithm,…

Learning Achievement

Competence

Course prerequisites

Fundamentals of probability/statistics, linear algebra and computer science (data structures and algorithms)

Grading Philosophy

Final exam

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Eric Gaussier

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

Site for Inquiry