The Elements of Statistical Learning textbook is available for free. It is a classic, widely-used textbooks for statistics and machine learning. Here is a far from complete list of some of the topics:
- Supervised Learning
- Linear/Logistic Regression
- Model Selection
- Neural Networks
- Support Vector Machines
- Random Forests
- Unsupervised Learning
As you can see, the book is quite extensive.
Note: This book has been available for a quite a while, but I realized I have not added a link to it on my blog.
Top ten algorithms in data mining (2007) [pdf] | Hacker News.
The discussion below the link is also very good.
If you are curious, here are the 10 algorithms, and the paper is displayed below.
- Naive Bayes
Microsoft has been doing machine learning for a long time, and it looks like some of those efforts might soon pay off. How do you think this will work out for Microsoft?
Microsoft Renews Relevance With Machine Learning Technology – NYTimes.com.
David Barber, Computer Science Professor at University College London, is still offering his textbook, Bayesian Reasoning and Machine Learning, for free. This text looks quite extensive. The website also includes matlab code for many of the algorithms in the book.
Michael Cutler, cofounder of TUMRA, gave a nice talk to the University of Oxford Computer Science Department. The following quote from his talk sums up his idea.
Given a choice between a “best guess” now, and a “marginally better” answer later, I’d take the best guess every time.
Many times, academic people focus a lot of attention on improving the accuracy of an algorithm, when the resulting solution is too slow for industrial purposes.
reference: TUMRA Blog