Data mining

What Hacker News says

1. gtaniOct 2014
   sure, there's 2 sides, the maths and the programming/systems

   ________

   the math is mostly the same math that physics and EE majors do for first 2 years, plus probability/stats:

   https://www.seas.harvard.edu/courses/cs281/#schedule

   http://www.zipfianacademy.com/blog/post/46864003608/a-practi...

   http://fastml.com/math-for-machine-learning/

   ________

   the general purpose programming languages mostly used are python, C++, java, and the statistical languages include R, matlab.

   there's lots of good threads at /r/machineLearning, including

   http://www.reddit.com/r/MachineLearning/comments/2i68f0/tack...

   http://www.reddit.com/r/compsci/comments/2jw14w/which_fields...

   also some books for people who haven't the rigorous math background yet are Intro Statistical Learning (James et al), the ones by Peter Flach, Steven Marsland, and Witten/Franke:

   http://www.amazon.com/Data-Mining-Practical-Techniques-Manag...

   http://www.amazon.com/Machine-Learning-Algorithmic-Perspecti...

   Show morethread
2. _dpsAug 2011
   I recommend starting with Witten and Frank's "Data Mining: Practical Machine Learning Tools and Techniques", for two reasons:

   1) your mathematical background will make it easy for you to spot the areas where they gloss over important theoretical details (e.g. they don't treat Mercer's theorem deeply, if at all if I recall correctly). This will keep you from making rookie mistakes like picking your own strange kernel functions for SVMs (all of this will make sense to you after reading W+F)

   2) W+F is accompanied by Weka, which has decent-but-not-great implementations of a wide variety of algorithms in an open-source toolkit with a functional (though not particularly usable) GUI. You can be up-and-running on test problems from the the UCI Machine Learning repository in 20 minutes.

   Russell and Norvig is a good reference book, but my strong suggestion is to grab W+F, Weka, and some data from UCI's ML repo (http://www.ics.uci.edu/~mlearn/). Use Weka to run the standard algorithms from W+F on real data.

   After that things can get as complicated as you want. I'm personally betting that graphical models (see Koller's "Probabilistic Graphical Models") will continue to grow in importance.

   On the statistical side, I strongly recommend "Applied Linear Regression" by Weisberg to get a sense for the kinds of idea statisticians bring to the party (significance, parameter intervals, chained analysis like ANOVA).

   Hope that helps :-)

   Edit* spelling errors

   PS: Almost forgot MacKay's excellent "Information Theory, Inference, and Learning Algorithms" (free as pdf). It does a great job linking coding, ML, inference, and bayesian probability together. Read at least as far as the section linking K-means clustering to Expectation-Maximization over a Mixture of Gaussians, it's worth it.

   Show morethread
3. cschmidtMar 2011
   If you want a "less math" machine learning book, I like these two:

   Data Mining: Practical Machine Learning Tools and Techniques, Third Edition

   Ian H. Witten, Eibe Frank, Mark A. Hall

   http://www.amazon.com/Data-Mining-Practical-Techniques-Manag...

   Machine Learning: An Algorithmic Perspective

   Stephen Marsland

   http://www.amazon.com/Machine-Learning-Algorithmic-Perspecti...

   Show morethread
4. kanakFeb 2011
   Definitely depends on your math background (knowledge of analysis and linear algebra seems to be particularly helpful).

   Witten's Data Mining is a very good beginner's book (has very little math, but lots of good explanations and discussion of real life issues).

   Bishop's book is excellent, but it's easy to get lost if you don't have the mathematical background.

   Duda, Hart & Stork's Patten Recognition book is also very well organized and has one of the best first chapters in any machine learning book. But it too requires mathematical background to be fully appreciated.

   Hastie & Tibshirani's book is written by people from a statistical background, and is very very mathematical. I haven't progressed beyond Chapter 2, and I'm working on improving my math skills before I get back into it.

   -- For NLP, a very good intro is the NLTK book.

   Jurafsky & Martin's book covers more NLP topics, but Manning and Schutze cover statistical portions in depth. I think you should just read both :D.

   Show morethread
5. cschmidtFeb 2011
   I would cover some fairly basic topics:

   1. Naive Bayes (with review of probability needed to understand)

   2. Linear Regression

   3. Decision Trees (for a nonlinear model)

   4. Boosting (and why it doesn't overfit much)

   5. k-means (and other clustering)

   Be sure to emphasize cross validation or a holdout set to evaluate all these models.

   The new edition of the "WEKA" book would be a nice text, rather than the original papers.

   Data Mining: Practical Machine Learning Tools and Techniques, Third Edition Ian H. Witten, Eibe Frank, Mark A. Hall

   Show morethread
6. woosterJul 2010
   A fair question.

   There are duplicates (Witten and Frank's "Data Mining", which is excellent), and some of the books he lists as favorites I would say are utter garbage. Some of them are so bad, I don't see how anybody smart could have read them and concluded they were worth reading.

   That said, as far as the idea of reading that many books, I don't see how it's out of line. I read a lot, but not nearly as much as a professional researcher such as Norvig would. I have several hundred CS textbooks I've either read or skimmed pretty thoroughly, and most of my favorite professors had many more which they'd read cover to cover.

   Show morethread