Statistical Data Mining Tutorials

“The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.

These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. They include regression algorithms such as multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and neural nets. And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning…”

http://www.autonlab.org/tutorials/

Lisp for C++ programmers

“One old good friend of mine, whom I respect a lot and who is a very good C++ programmer, recently asked me to give him an example of how it’s possible make new language features in Lisp. He’s aware of Lisp’s ability to invent new syntax, and he’s also excited about C++11. So he wonders how is that possible to introduce new syntax into your language all by yourself, without having to wait for the committee to adopt the new feature.

I decided to write this article for C++ programmers, explaining core Lisp ideas. It’s a suicide; I’m sure as heck that I’ll fail. Great number of excellent publications on Lisp for beginners exist, and still there are people who cannot grasp what’s so special about it.

Nevertheless, I decided to try. Yet another article with introduction to Lisp won’t harm anybody, nor will it make Lisp even less popular. Let’s be honest: nobody reads this blog, anyway. :)…”

http://prog-elisp.blogspot.ru/2013/10/lisp-for-c-programmers.html

Intro to pandas data structures

“What follows is a fairly thorough introduction to the library. I chose to break it into three parts as I felt it was too long and daunting as one.

Part 1: Intro to pandas data structures, covers the basics of the library’s two main data structures – Series and DataFrames.

Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. It shows how to inspect, select, filter, merge, combine, and group your data.

Part 3: Using pandas with the MovieLens dataset, applies the learnings of the first two parts in order to answer a few basic analysis questions about the MovieLens ratings data…”

Lua: Good, bad, and ugly parts

“I have come across several detailed lists that mention good and not-so-good parts of Lua (for example, Lua benefits, why Lua, why Lua is not more widely used, advantages of Lua, Lua good/bad, Lua vs. JavaScript, and Lua Gotchas), but I found that some of the features that tripped me or that I cared about were not listed, so I put together my own list. It is far from being comprehensive and some aspects of the language are not covered (for example, math and string libraries), but it captures the gist of my experience with the language…”

http://notebook.kulchenko.com/programming/lua-good-different-bad-and-ugly-parts