Friday, June 22, 2012

My last seminar and Non Parametrics for the Lay Man

In The University of Tokyo, we have to present 3 seminars through the course of our PhD's, the first one is a survey, the second one is your midterm evaluation and the last one is another survey.

They are also called Rinko (輪講), It roughly translates as reading (or discussion) circle.

The format of the seminar is the following:

  • Presenters: 3 Presenters (PhD and Master students)

  • Audience: A room packed with about 70 students from different research groups, you have people from every background in Electric Engineering and CS, information, semiconductors, power systems, computer science, robotics. You also have your Prof, and usually, a couple other Professors who might or might not be related to your topic. They are the Prof. of the other 2 guys presenting with you. The administration tries to have Prof. on related fields.

  • Time: 25 minutes to present your slides
  • Materials: You have to present a Paper-Like document of  at most 8 pages with your topic. Slides for your presentation
  • Questions: 5-10 minutes at the end, either from the Prof or the Students.

Given this format, it is tricky to introduce to them a new topic like Non Parametric Bayesian methods . I had to decide either to spend my time trying to teach them the inner workings of things like the Dirichlet Process or the Gamma Process, and then try to explain how things like Naive Bayes or LDA benefit from this; or spend my time showing them some cool applications and areas where they could use it, prepare an easy reading paper, not to deep and with lots of citations for them to go look if they were interested.

Needless to say, I went with the last option, and here it is, my version of what I'd call Non Parametric Bayesian Methods for the layman.

This document is NOT TO LEARN Non Parametric Methods, but rather to see how can you use it and have a friendly introduction to the topic, I introduced basic things about DP and IBP, but I did not mention things like inference or Gibbs Sampling.

If you wish to learn DP or IBP, you can always go to the papers I cite. But I commend you not to use this as your main source of information, I know I wouldn't do it.

I would like to make this a living document, so if you have suggestions or ideas, I can always add them to the final paper, I left a ton of things out of the paper due to space constrains (8 pages). So send me an email or a tweet if you wish to add something,  or point out a typo, I'm sure it's full of those. @leonpalafox



Note: These slides and document are free for you to use, distribute and modify as you wish, if you want to give me a little credit, just point out to here or my webpage, and it would be more than enough


1 comment:

  1. Hi Leon,

    Thanks for sharing. I would like that I could had been there but my telemetry machine is not working.

    I believe that your rinko was good but I remember that in many rinkos, if I would be without laptop, I would be very bored :-D