What are the prerequisites for studying Machine Learning?

Question

I was always fascinated by the topic of Machine learning until I decided to teach myself how to do it. So I came through a course provided by Stanford published online. However I was shocked of the amount of math it contained. So what is the mathematical background I should have to be able to understand the algorithms of machine learning? Are there any libraries that abstracts all the maths and focuses on actually designing a software capable of learning?

Answer 1

Much like 99% (don't quote me on the number) of the computer-science related topics, the theoretical foundation of Machine Learning often involves a lot of math... none-the-less, it shouldn't be that difficult to pick up on some basic ML algorithms even without in depth knowledge of calculus.

There are various machine learning libraries out there:

• Weka
• Java Machine Learning Library (Java JML)
• Apache Mahout
• Others...

I would say that you should start by trying to build your own simple ML algorithm: maybe a Neural Network or a Genetic Algorithm. Successfully building one will make quite a difference in your understanding... especially since given a specific problem, you might have to customize the ML algorithm quite a bit. Knowing how it works, from the ground up, is going to allow you to make any modifications that you deem necessary.

Answer 2

Here's an episode of .NET Rocks! talking about machine learning, and a small library to play around with

Answer 3

This article has a really nice review of the background information required for a good understanding of machine learning. The article also recommends good books for each topic.

Answer 4

Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran is an AWESOME book! Toby builds simple implementations of all-times Machine Learning classics: Neural networks, support vector machines, genetic algorithms, clustering. All that with simple explanations on how and why they work. As a bonus, all the examples are in Python! But even if you don't know Python you'll understand the book. I highly recommend it

Answer 5

See here for some background texts on machine learning: http://bumphunting.blogspot.com/2009/07/what-are-good-prequisite-textbooks-for.html

Answer 6

Core:

• Linear Algebra (learn to use vector and matrix, factorization, eigenvalue)
• Numerical methods (solutions to matrix equations, and introduction to some simple numerical optimization algorithms)
• introduction to probability (and conditional probability)

Depends on prof.

Answer 7

Linear algebra and (basic) statistics.