UE Advanced algorithms for machine learning and data mining

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Degrees incorporating this pedagocial element :

Description

  • 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,…

Recommended prerequisite

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