Mabble Rabble: Machine Learning

10 October 2013

Machine Learning is a branch of Artificial Intelligence which studies systems which are able to learn from a given set of data as examples. It can be split into two main strands supervised learning (classification) and unsupervised learning (clustering) and in some cases semi-supervised. Some algorithms don't fit into either category explicitly. For most applications learning algorithms need to be carefully tested against customized data for each specific context. Also, the choice of a learning algorithm is important as they all have merits and drawbacks. However, hybrid approaches are seen to generally work the best as they are able to reflect off each other in the reduction of error margins and increased accuracy of learning. Or, perhaps, one can head in the direction of deep learning. The following is a list of some useful books, tools, and free courses in the area.

Books:

An Introduction to Support Vector Machines

Reinforcement Learning: An Introduction

Bayesian Reasoning and Machine Learning

The Elements of Statistical Learning, Data Mining, Inference, and Prediction

Gaussian Processes for Machine Learning

Introduction to Machine Learning

Machine Learning

Information Theory, Inference, and Learning Algorithms

Pattern Classification

Machine Learning: A Probabilistic Perspective

Pattern Recognition and Machine Learning