Teaching‎ > ‎

Machine Learning and Data Mining 2016-2017

Obiettivi

Progettare, realizzare e valutare sistemi di apprendimento automatico e modellazione statistica.

Prerequisiti

Basi di statistica e algebra lineare.

Argomenti

  • Data analysis with R software
    • Data visualization
  • Supervised learning
    • Linear regression
    • Classificatioon: logistic regression, linear discriminant analysis (LDA)
    • Tree-based methods: regression and classification trees, random forest
    • Support Vector Machines (SVM)
  • Model assessment and selection
    • Bias and variance
    • Regularization
    • Cross validation
  • Unsupervised learning
    • Principal Component Analysis (PCA)
    • Factor analysis
    • Clustering (K-means, hierarchical)
  • Evolutionary computation
    • Genetic Algorithms (GA)
    • Genetic Programming (GP)
    • Grammatical Evolution (GE)
  • Text mining
  • Recommender systems

Modalità d'esame

Progetto (tesina) e esame scritto.

Calendario lezioni e date d'esame

Lezioni


Testi

  • Kenneth A. De Jong. Evolutionary computation: a unified approach. MIT press, 2006
  • Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning: Data Mining, Inference, and Prediction. Springer, Berlin: Springer Series in Statistics, 2009.
  • Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning, with applications in R. Springer, Berlin: Springer Series in Statistics, 2014.
Subpages (1): Progetto
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Eric Medvet,
Jan 23, 2017, 9:09 AM