Machine Learning - Waseda University - Summer 2011
If you want to arrange a meeting or have any question, just send me an email at the
following address: arnaud at cs dot ubc dot ca
Prerequisites:
Linear algebra, calculus, probability theory, programming (Matlab).
Textbook
I will follow primarily the following textbook:
Chris Bishop, Pattern Recognition and Machine Learning,
Springer-Verlag, 2006.
I will also use some material from the book of my UBC colleague Kevin Murphy,
Machine Learning: a probabilistic approach
that will be published by MIT Press next year.
You might want to have a look at the freely available book by David Barber, Bayesian Reasoning and Machine Learning
Basics
Programming language: The programming language of the course is
Matlab. I strongly recommend you follow this link and become familiar with Matlab.
Maths : If you do not feel comfortable with calculus, linear algebra and probability then please do read the following material
* Cribsheet
* Linear algebra: A review Another review
* Probability: Probability theory refresher, another review
Potential projects here
Acknowledgments
I will reuse some of the material developed by my UBC colleagues Nando de Freitas
and Kevin Murphy.
Announcements
- Don't forget to tell me very soon on which project you are planning to work.
Handouts
- Lecture 2 (Tuesday 14th June) - Regression: Slides 3
* 20th June: I've corrected in the slides
- page 7 and page 8, definition of the matrix Phi (I was starting numeroting the columns from index 0 instead of 1)
- page 21: definition of the prior p(w). You have (beta*lambda)^-1 appearing instead of (beta*lambda)
- page 32: reformatted the text so that it is not cut anymore!
Reading: Chapter 3 of the book of Bishop.
- Lecture 3 (Tuesday 20th June) - Regression, Cross-Validation and Bayes Linear Regression & Model Selection Slides 4
- Lecture 4 (Tuesday 27th June) - Logistic regression and Kernel methods Slides5 Slides6
Reading: Chapter 6 of the book of Bishop.
Additional
reading: Introduction to
Gaussian processes
- Lecture 5 (Tuesday 5th July) - Markov chain Monte Carlo methods Slides 7
Reading: Chapter 11 of the book of Bishop
You can also read An
introduction to MCMC for machine learning Pdf file
- Lecture 6 (Tuesday 12th July)- Sequential Monte Carlo methods Slides 8
Reading: An introduction to SMC