2011-ML
Lecture Notes
Introduction
Welcome
Machine learning is an exciting recent technology. In this class we learn about state of the art and gain practice implementing and deploying ML algorithms.
Examples of ML algorithms in practice include:
- Search engines like Google and Bing ranking web-pages
- Facebook or Apple's photo-tagging application
- Spam filtering
The dream of ML is to create machines that are as intelligent as humans, but this goal is a long way off. Many researchers believe that the best way to that goal is to come up with learning algorithms that mimic how the human brain learns.
In this class you learn about state of the art machine learning algorithms. But knowing the algorithms and the math isn't much good without also knowing how to get the stuff to work with problems that you care about. So we also do exercises.
Why is machine learning prevalent today? Machine learning grew out of work in AI and is a new capability for computers. We know how to program computers to do well defined things like finding the shortest path between A and B, but we didn't know how to do more sophisticated things like ranking web pages, identifying friends in pictures, or filtering spam. There was a realisation that the only way to do these things was to have the machine learn do it by itself.