Machine Learning

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Information

Machine Learning was offered in Fall 2011 and is going to be offered again on (was January 23, 2012) now April 23, 2012.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.

Topics include:

1. Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
2. Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
3. Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Prerequisites

Linear algebra and also the course includes programming assignments and some programming background will be helpful.

Recommended reading

Video materials

Lecture notes

Lecture notes with diagrams by Alex Holehouse

Software

Further studies

The Elements of Statistical Learning (12MB)