This course will review the past, present, and future of educational data science, from early computer-based intelligent tutors to machine-learning based auto-grading to large-scale learning analytics. We will also explore a range of applications.

This is a “reading course,” meaning that students will read and present papers from the technical literature to the rest of the class in a lively debate format.  Discussions will aim at identifying common themes and important trends in the field.  Students will also obtain valuable hands on experience through a group project.

• Location: Zoomland
• Time: Friday 245pm
• Instructor: Richard Baraniuk
  2028 Duncan Hall
  Office Hours: By appointment
• Prerequisites: Required: Linear algebra, introduction to probability and statistics, familiarity with a programming language such as Python, R, or MATLAB.  Desired: Knowledge of machine learning, signal processing, optimization, and deep learning
• Course Website:  Piazza Course Management Site (It is mandatory that you use this site; all official announcements will be made there)