I’m taking the machine learning course being taught by Andrew Ng at Coursera. At times it’s a bit light on the theory for my tastes, which is understandable, so I’ve been looking to other sources. One that I’d come across previously that I ended up buying is Ethem Alpaydin’s Introduction to Machine Learning.
But Alpaydin’s book has its own problem: a relatively small number of exercises, and no data. So it seems useful to find more exercises and people who have written data-based exercises to go with the book. The obvious place to do this is to find courses that have been taught using this book, so I decided to compile a list of such courses. I make no claim that this list is anywhere near a complete list; it was compiled by half an hour of Googling. But if I was going to make such a list, it seemed good to make it available.
Incidentally, I think in general it would be good to have lists of web pages of “courses taught using book X” available, both for learners (who might want to see supplementary resources, get a sense of which sections of a book are more or less important, and so on ) and for teachers (to see how others have organized their courses).
Here’s the list:
- Alan Yuille, Introduction to machine learning, UCLA Stat 161/261, Spring 2010.
- Ahmed Elgammal, Machine learning, Rutgers 198:536 (CS), spring 2007.
- Joakim Nivre, Machine learning for NLP, Uppsala fall 2011.
- Thorsten Joachims, Machine learning, Cornell CS 478, spring 2008.
- Dan Lizotte, Introduction to machine learning, Reykjavik, spring 2007.
- Alexander Partzin, Machine learning, Tel Aviv, fall 2007.
- Berrin Yanikoglu, Machine learning, Sabanci University (Turkey) CS512, fall 2011.
- Andrea Danyluk, Machine learning, Williams CS 374, spring 2011.
- Shan-Hung Wu, Machine learning, National Tsing Hau University, spring 2012.
- Zheng-Hua Tan, Machine learning, Aalborg (Denmark), spring 2011.
- Kevin Murphy, Machine learning, British Columbia CS 340, fall 2006.
- Jugal Kalita, Machine learning, University of Colorado at Colorado Springs CS 586, spring 2010.
Incidentally, a lot of courses in this area seem to recommend more than one text, because it’s a rapdily growing area. Others that seemed to be mentioned a lot in the same breath as Alpaydin are Bishop, Pattern Recognition and Machine Learning and Mitchell, Machine learning.
(Thanks to Brent Yorgey for a correction.)
Yes, I agree that it’s great to see which other courses are using a text. Conversely, I hate it when courses go behind learning-management system walled gardens. It’s not very helpful to other people around the world.
This is a big part of the reason why I’ve put as much as I can of my courses on publicly-accessible web pages. Essentially all I use the walled gardens for is to post student grades and to post solutions to homework assignments. (The latter is because many of my homework problems have been from textbooks.) I always found it useful to see what other people were doing and I want to pay that forward.