Intro To Machine Learning

CS 429/529

All the course sections are closed

Dear students, unfortunately, there is no more space in the course for new or incoming students. The room that we are using has a capacity of 39 seats. This cap is imposed by the fire department. We cannot let more students in, as it would be in violation with the fire safety code, which could result in serious consequences and fines for the University. I'm sorry about that, and I encourage all the new students to explore other empirical courses offered by the department or to wait and enroll in ML for the next year



  • Trilce Estrada, Assistant Professor
  • Email: please use piazza to communicate
  • Office: Farris 2390
  • Office hours: Tuesday 2:00 to 3:30 PM, Thursday 11:00 AM - 12:30 PM, or by appointment

Teaching Assistants

  • Name: Jamie Wingo
  • Email:
  • Office: Farris 2085
  • Office hours: Monday and Friday 1:00 PM - 2:30 PM

Course description:

Introduction to principles and practice of systems that improve performance through experience. Topics include statistical learning framework, supervised and unsupervised learning, performance evaluation and empirical methodology; design tradeoffs.

For more information look at the Syllabus