This is an introductory course on machine learning that covers the basic theory, algorithms, and applications. Machine learning enables computational systems to adaptively improve their performance with experience accumulated from the observed data. It has become one of the hottest fields of study today, with applications in engineering, science, finance, and commerce. The course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion.

Update: 看了第一课的内容，觉得这个课应该会很有意思。lecture 长度大概在一小时左右，还有挺长的 Q&A 环节，至少第一次 lecture 里大家提的问题都是非常犀利的。课程的安排大致是按照这样的顺序来的：

1. What is Machine Learning?
2. Can we do it?
3. How to do it?
4. How to do it well?

Yaser points out some nicely videotaped machine learning lectures at Caltech. Yaser taught me machine learning, and I always found the lectures clear and interesting, so I expect many people can benefit from watching. Relative to Andrew Ng’s ML class there are somewhat different areas of emphasis but the topic is the same, so picking and choosing the union may be helpful. hunch.net