ELEN0062 - Introduction to machine learning (iML)

Random ML quote

With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real… Big data isn’t about bits, it’s about talent.

Douglas Merrill



Installation 04 Oct. 2017 Python, Numpy, Scipy, Scikit-learn installation with anaconda
11 Oct. 2017

Bring your laptop if you want to assist
A crash course about the Pythonsphere
If you have questions regarding the exercises, you can email me.

First assignment

Q&A 25 Oct. 2017 Question/answer session regarding the first assignment.
Q&A 18 Oct. 2017 Question/answer session regarding the first assignment.
31 Oct. 2017

Don't forget to submit your first assignment.
You can go up to 5 pages (with 1 page for question 3.1)

Second assignment (Antonio Sutera is the reference TA for this assignment)

Cheat sheet for ML in Python

Check out datacamp for more.

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:

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.

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. As a consequence, internet is bursting with resource on the topic, from the simplest models (multi-layer perceptron) to the most advanced architectures (such as GANs), going through more classical ones (such as Convnets and LSTM).

Learning theory (Bias/Variance...)

Support Vector Machines

Unsupervised learning


There are many YouTube channels about ML. Here are a few:


Machine learning requires a solid background in maths, especially in linear algebra, (advanced) probability theory and (multivariable) calculus. There are even more resources on those than on deep learning. Here is a short selection, which emphasizes intuition.

Linear algebra

  • 3 brown 1 blue serie on linear algebra
  • If you prefer paper (or PDF): Practical Linear Algebra: A Geometry Toolbox 2nd Edition by Farin, Gerald, Hansford, Dianne. A K Peters/CRC Press (2004)


Last modified on October 31 2017 10:24