Randomized Algorithms for Data Science
Instructor: R. Teal Witter. Please call me Teal.
Class Times: Tuesdays and Thursdays from 2:45 to 4:00pm in Kravis 164.
Office Hours: Mondays and Thursdays from 12:30 to 2:00pm in Adams 213.
Problem Sets: Your primary opportunity to learn the material will be on problem sets. You may work with others to solve the problems, but you must write your solutions by yourself, and explicitly acknowledge any outside help (e.g., websites, people, LLMs).
Quizzes: There will be short quizzes at the beginning of (randomly) selected classes. These quizzes will test your understanding of the problem sets and the concepts from the prior week.
Exams: The two midterm exams are the primary method of assessing your understanding of the material.
Project: The project offers a chance to explore an area that interests you, practice writing high quality code, and develop your ability to communicate technical ideas to an audience.
Resources: This class is based on Chris Musco’s phenomenal algorithmic machine learning and data science course at NYU. While we do not have a textbook, I have prepared written notes for every lecture.