Introduction to Machine Learning


Teacher coordinatorGilles Gasso
Teacher(s)Gilles Gasso, Benoît Gaüzère
TeachingLectures : 21h Exercises : 21h
Web site

Aim and objective

  • Develop an appreciation for data exploration and visualization
  • Understand a wide variety of learning algorithms
  • Understand how to apply a variety of learning algorithms and related software toolboxes to data
  • Understand how to perform evaluation of learning algorithms and model selection

Outcome learning

  • INSA reference data :
    • Concevoir un système d'ingénierie des données [3P]
    • Optimiser un modèle [3P]
  • CNISF reference data :
    • J10P [2P]
    • J40K [1P]
1 - Notion, 2 - Concept, 3 - Application, I - fully, P - incomplete

Course description

  • Introduction to Statistical Learning
  • Optimization tools for Machine Learning
  • Unsupervised learning : Principal Component analysis (PCA), Clustering methods (Agglomerative clustering , K-Means, Mixture Model)
  • Classification Methods : Theory of Bayesian Decision, Logistic Regression, Support Vector Machine, over-fitting, model tuning
  • Introduction to large scale learning


Notion of statistics and matlab programming


  • Christopher Bishop, Pattern Recognition and Machine Learning, 2006
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning (Data Mining, Inference, and Prediction), 2009
  • Richard Duda, Peter Hart, David Stork, Pattern Classification,


  • Final Exam: 50%
  • Practical exam: 50%