STA 325: Data Mining and Machine Learning

This is a rough schedule for the course and will be updated regularly. Please check this frequently for adjustments. Announcements will be posted here and made in class. It will be up to you to keep up to date on all class announcements and web announcements made for the course. Read along in Hoff before coming to class.

The course syllabus can be found here Syllabus: things you need to know about the course!

All announcements will be posted on Sakai.

Please check for updates to the course notes regularly as they are being written and updated quite frequently. (I tend to update them before or after each class on Tuesday's and Thursdays).

Lecture notes

  • Find a quick review of R that I expect you should have no trouble completing.
  • Module 0: An introduction to data mining and machine learning
  • Module 1: An introduction to data mining and machine learning, Part II

  • Module IR: Information Retrieval

  • Module IR: Locality Sensitive Hashing

  • Module 2: Introduction to Statistical Machine Learning

  • Module US: Unsupervised Learning (Ch 10 ISLR)

  • Module 3: Linear Regression
  • Module 4: Classification: Logistic Regression and Linear Discrimminant Analysis
  • Module 5: Resampling Techniques
  • Module 8: Decision Trees