Machine Learning: Resources

Communication

Programming, Systems, and other related topics

In completing your programming assignments this semester, you may choose to write in Python or Java.
If you plan to do programming assignments with your tutorial partner, you might consider Teletype for Atom, as it will allow you to collaborate in a healthy, socially distanced way.
Mary has put together a web page describing online resources for remote work.

Writing up and turning in your work

Problem sets
- We do not require that your problem sets be typeset using LaTex. However, if you are going to write up your work by hand, it must be neat, clear, and easy to read.
- Please do not put your name on your problem sets. Instead, use the last four digits of your Williams ID.
- Please electronically turn in a single pdf with your problem set solutions each week. (If you need a recommendation for a scanning app, let us know.)
- If you worked with anyone on your problem set, please let us know at your tutorial meeting.
- Each week we will set up a google form where you can upload your completed work. You can find those links on the Schedule and Assignments page as well as in Glow.
Reading responses, essays, and long text analyses
- These must be typed up (using, for example, LaTex, Word, etc).
- You may put your actual name on these homework documents.
- Please electronically turn in a pdf file.
- Each week we will set up a google form where you can upload your completed work. You can find those links on the Schedule and Assignments page as well as in Glow.
Code
- Include your name(s) on all programming assignments.
- Each week we will set up a google form where you can upload your completed work. You can find those links on the Schedule and Assignments page as well as in Glow.

The books listed here are available electronically. This is just a very small sample of the books available on this topic.

in alphabetical order by author:

Alpaydin, Introduction to Machine Learning, third edition, MIT Press, 2014. [Online access available through our library.]
Chapelle, Scholkopf, and Zien, Semi-supervised learning, MIT Press, 2006. [Online access available through our library.]
Cristianini and Shawe-Taylor, An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge, 2000. [Online access available through our library.]
Daumé, A Course in Machine Learning, (available here).
Dougherty, Pattern Recognition and Classification, Springer, 2013. [Online access available through our library.]
Goodfellow, Bengio, and Courville, Deep Learning, MIT Press, 2016.
Hastie, Tibshirani, Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009. [Online access available through our library.]
Sammut and Webb (eds), Encyclopedia of Machine Learning, Springer, 2010. [Online access available through our library.]
Schapire and Freund, Boosting: Foundations and Algorithms, MIT Press, 2012. [Online access available through our library.]
Witten and Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, 2005. [This, as well as the 2011 version, are available electronically through our library.]