Introduction to Machine Learning

Lecture 1

  • What is machine learning?

  • Exemples of problems

  • Typology of ML concepts

  • Decision tree

  • k-NN

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Readings:

Lecture 2

  • Linear models

  • Loss functions

  • Regularizaton

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Readinng:

  • Chapter 3 in “Machine Learning - A First Course for Engineers and Scientists” (Lindholm, et.)

To go further:

Lecture 3

  • Deep learning

  • Multilayer Perceptron (MLP)

  • Convolutional neural networks (CNN)

  • Regularization

  • Training

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Representation learning visualization

Lecture 4