Rémi Flamary

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MAP654I Practical introduction to Machine Learning


Course overview:

  • Data and Machine Learning problems [PDF][PDF_2x2]
    • Data properties and visualization
    • Pre-processing
    • Finding your Machine Learning problem
  • Unsupervised learning [PDF][PDF_2x2]
    • Clustering
    • Density estimation and generative modeling
    • Dictionary learning and collaborative filtering
    • Dimensionality reduction and manifold learning
  • Supervised learning
    • Bayesian decision and Nearest neighbors
    • Linear models nonlinear methods for regression and classification
    • Trees, forest and ensemble methods
  • Machine learning in practice
    • Performance measures
    • Models and parameter selection (validation)
    • Interpretation of the methods

Practical sessions

Practical sessions will require a working Python environnement with the libraries Numpy/Scipy and Matplotlib installed. You can get such an environnement for Windows/Linux/MacOSX on Anaconda.

Here is a list of nice references and Python tutorials :


  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics. [PDF]
  • Bishop, C. M. (2006). Pattern recognition. Machine learning, 128(9).
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. [URL]
  • Ng Andrew, Coursera [URL]