# 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:- Andrew Ng's online course (Standford): The most popular online course on ML. Archived from coursera.
- Pedro Domingos' online course (Washington).
- Reza Shadmehr (Baltimore) and his slides.
- Jeffrey Ullman's course on mining massive datasets (Standford) based on his reference book. Not everything is related to the course though.

### Classification and regression trees

### Linear regression

### 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.- Graham Taylor: An Overview of Deep Learning and Its Challenges for Technical Computing (2014)
- Geoffrey Hinton: Introduction to Deep Learning and Deep Belief Nets (2012)
- Geoffrey Hinton: The Next Generation of Neural Networks (2007)
- Leon Bottou: Multilayer Networks series
- A simplified version of Backprop illustrated.
- An illustrated taxonomy of learning networks.

### Learning theory (Bias/Variance...)

### Model assessment and selection

### Support Vector Machines

- Visualizing the kernel trick
- A couple of videos about constraint optimization (by Khan Academy):

### Ensemble methods

### Feature selection

### Unsupervised learning

### Misc.

There are many YouTube channels about ML. Here are a few:- Sentex: A bit of everything
- Derek Kane: A bit of everything
- Welch Labs: A few videos about Neural Nets
- Two minutes papers: Many articles relate to (applications of) ML
- Siraj Raval (this guy is crazy)
- Introductory online course on ML (covers linear/logistic regression, decision trees/random forests, basics on neural networks and a clustering).