Description

This course is an introduction to the foundations behind modern data analysis and machine learning. The first part of the course covers selected topics from probability theory and linear algebra that are key components of modern data analysis. Next, we cover multivariate statistical techniques for dimensionality reduction, regression, and classification. Finally, we survey recent topics in machine learning, in particular, deep neural networks.

Logistics

• Time: TTh 2:00 - 3:15 PM
• Location: Thornton E316 (lectures will be recorded)
• Instructor: Miaomiao Zhang (mz8rr AT virginia DOT edu)
  • Office Hours: Fridays 3:30PM-4:30PM (@Rice Hall 300)
• TA: Md Khairul Islam (mi3se AT virginia DOT edu)
  • Office Hours: Mondays 11AM-1PM (@Rice Hall 442)
• TA: Maria Ana Cardei (cbr8ru AT virginia DOT edu)
  • Office Hours: Wednesdays 11AM-1PM (Olsson (Link Lab) 225)
• TA: Zhenyu Lei (vjd5zr AT virginia DOT edu)
  • Office Hours: Fridays 12PM-2PM (@Rice Hall 442)
• Textbook: Some readings from a free online resource Mathematical Foundations for Data Analysis, by Jeff Phillips
• Prerequisites: You should be comfortable programming in Python (CS 2110 or equivalent is sufficient)
• Software: All homeworks will be done in Jupyter

Acknowledgement

This class was inspired by Jeff Phillips’ University of Utah course, CS 4964: Foundations of Data Analysis