Friday, July 1, 2011

Where should I start, what should I do?

So you are all set in a Machine Learning Grad Course (I'll leave the admission niceties to you, since they change exponentially from country to country)

If you're lucky and have a good adviser, you'll probably have a project right away, but if not?

A lot of students have the feeling that they are alone, stranded and unwanted. And Machine Learning is no exception. Sometimes you won't really know where to start looking. Even if you have a project, to actually start doing things may take you some time.

In case you do not have a project, try looking around for what people are doing in you laboratory. It's always a good idea to try to work with someone, since you'll have feedback and a sense of commitment to other person. These simple things will help you progress in your research.

You can always go with your professor and see what he's working in (remember I told you it was important to have an active researcher as a professor) and offer your help. Even coding simple things are a great help for him, and give you a pretty good insight on advanced work and which problems need solution.

You should also pick up basic books on the topics you have interest in. A very good introductory book to the different areas of ML is Bishop's Book, (Be aware that you'll need a good background of Linear Algebra, Probability and Calculus to grasp most of the contents.). In a future post we will put a detailed list of which books may help you in your research.

Try also to look for the most recent conferences in a topic you like, see what the world is working on, and what unsolved problems are there. If you're lucky, your professor may pay for you to go to some of these conferences, even if you have no accepted papers.

But do you want to solve fundamental problems, or do you want to solve technical problems? Different problems have different sources.

There is another thing to post next time for choosing your research. Do you want to apply Machine Learning, or do you want to develop ML algorithms?

See you next time.

Don't forget to pay a visit to my webpage and leave some comments here.

1 comment:

  1. I'm not sure if the book of Bishop is a good introductory book for beginners because it's mainly targeted to graduate students that already know a bit of those subjects. Maybe the book The Elements of Statistical Learning of Hastie et al is better to start with.

    The book of Bishop is also too biased towards Bayesian statistics and the world is not just Bayesian. Even so I like it and it's my desk companion, but hard to digest for beginners.