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Comment by yummyfajitas | Hacker News Reader

Why wouldn't it be machine learning? It certainly fits the definition of supervised learning (aka regression).

Admittedly you do usually learn it in unsexy statistics classes rather than sexy machine learning classes...

The lines have always been a little blurry for me. You have statistics, machine learning, data mining, artificial intelligence. It all seems to overlap heavily. I tend to consider statistics and data mining to be concerned mostly with classical statistics, and machine learning and artificial artificial intelligence to be concerned more with bayesian methods, and algorithms. That being said, logistic regression seems like the quintessential machine learning technique. So who knows, any experts care to comment?

As someone who did graduate work in Bayesian methods for a statistics master's degree, I take offense to saying machine learning is not a concern of statistics and data mining (but not really)! The hesitance towards Bayesian methods seems more related to the discipline, and it seems that places that call what they do "machine learning" tend to be less hostile towards the explicit subjectivity of Bayes (I would highlight the word "explicit" in that sentence--Frequentism has it's fair share of subjectivity as well).

There was a great post on Stats.SE a few years ago about the difference between statistics and machine learning[1]. Leo Breiman once argued that statistics tends to focus more on model fitting and checking, while machine learning looked at prediction accuracy. The exchange between Andy Gelman and Brendan O'Connor is pretty funny. It has been my personal experience however that many people that apply a method that they brand as "machine learning" are not as bothered with assumptions as my fellow conservative statisticians.

But statistics and machine learning are quite similar in foundation. Barring the differences in terminology, as a professional statistician, I find I have as little difficulty read machine learning papers and algorithms as I do reading statistics ones.

http://stats.stackexchange.com/questions/6/the-two-cultures-...

All of these terms are used interchangeably, so the following definitions probably wont be too helpful in the real world, but in my experience:

Artificial Intelligence is an umbrella academic term which encapsulates the study and design of intelligent machines. It's not well defined because AI is evolving so rapidly.

Machine Learning is a branch of AI that is concerned specifically with learning from data; the results of learning are usually used to predict future events. (Think linear regressions, random forests, etc.)

Though not specifically a part of AI, Statistics is the field that formed many of the algorithms used in ML. Stats informs ML research design (e.g., how large of a sample size do I need), generates mathematical solutions from proofs and equations, etc. With the rise of big data, it's slowly merging with ML.

Data Mining is a mix of ML, Stats and Data Engineering. It's more concerned with structuring and extracting patterns from data than necessarily learning from it. It is often a task within an ML project.

Because it was done in pen and paper by Gauss 200 years ago :)

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