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UE Advanced algorithms for machine learning and data mining

Diplômes intégrant cet élément pédagogique :

Descriptif

  • 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,…

Pré-requis recommandés

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

Bibliographie

C.D. Manning, P. Raghavan and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, USA, 2008.

A. DasGupta. Probability for Statistics and Machine Learning. Springer, 2011.

I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.

C.M. Bishop. Pattern Recognition and Machine Learning. Springer Verlag, 2006.

Informations complémentaires

Méthode d'enseignement : En présence
Lieu(x) : Grenoble
Langue(s) : Anglais