INFO 1998

Introduction to Machine Learning, Fall 2023

Course Manager: Varun Gande
Course Email: Gmail
Lecture: Wednesdays 4:40-5:40 PM, Hollister 110
Discussion: ED Discussion
Grades: CMS
Lecturers: Varun Gande, Eric Guo, Jake Silver, Koji Kimura, Neha Kulshreshtha, Vincent Fong, Jacob Mayourian

Office Hours

Office hours will start after Wednesday, September 13th!
TA Date and Time Location
Deniz Bölöni-Turgut Mon 12pm-1pm Hollister 320
Mericel Tao Mon 1:15pm-2:15pm Upson 206
Jake Silver Wed 2:30pm-3:30pm Carpenter 104
Audrey Wang Thu 3pm-4pm Zoom
Eric Guo Fri 1pm - 2pm Hollister 110
Varun Gande Fri 4pm - 5pm Phillips 307

Enrollment Information

Enrollment for the course on Student Center begins around the first day of the course (tentatively, September 13). In the meantime, please fill out this form to be placed on our interest form list.

Unfortunately, this semester we are unable to accommodate for students who are at the hard credit limit, or have a scheduling conflict in Student Center.


INFO 1998 is a ten week, one credit, S/U only course.

The goal of this course is to provide you with a high-level exposure to a wide range of Data Science techniques and Machine Learning models. From the basics of getting your Jupyter environment setup, to manipulating and visualizing data, to building supervised and unsupervised models, this class aims to give you the base intuition and skillset to continue developing and working on ML projects. We hope you exit the course with an understanding of how models and optimization techniques work, as well as have the confidence and tools to solve future problems on your own.

The key to succeed in the course is to try out everything yourself. The lecture and assignments are designed with this in mind — we did our best to convey an experience in which you get a chance to work on many techniques and machine learning models. The course material is designed assuming you have no prior experience in machine learning, but does assume that you have been exposed to solving Computer Science problems at an introductory level (in any flavor / language). Accordingly, we will go over the basics of Python to make sure we are all on the same page. You are expected to put in an appropriate amount of effort to complete the assignments and projects.