ELEN0062 - Introduction to machine learning

Random ML quote

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment.

Alvin Toffler

Informations

Schedule

Supplementary material

Here is a very scarce list of supplementary material related to the field of machine learning. I tend to update this section when I come across interesting stuff but if you feel like you need more material on some topic, do not hesitate to ask!

Machine learning in general

There are tons of online and accessible material in the domain of machine learning:

Classification and regression trees

Linear regression

  • The geometry of Least Squares (1 variable)
  • Note that the ANOVA is a special case of linear models where the input variables are dummy one-hot class variables. Consequently, the basis vector of the column space are orthogonal and the problem reduces to many 1 variable least squares.

    Nearest neighbor(s)

    Artifical neural networks

    There have been three hypes about ANN. The first one was about the perceptrons in the 60s until it was discovered it could not solve a XOR problem. The second hype started with the discovery of backpropagation but it soon became clear that the large and/or deep neural nets were very hard to train. We are in the midts of the third one right now with `deep learning`: neural nets with several (many) invisible layers.

    Learning theory (Bias/Variance...)

    Model assessment and selection

    Support Vector Machines

    Ensemble methods

    Feature selection

    Unsupervised learning

    Misc.

    There are many YouTube channels about ML. Here are a few:
    Last modified on September 21 2017 13:51