INFO 1998
Introduction to Machine Learning, Spring 2024
Course Manager: Deniz Bölöni-Turgut
Course Email: Gmail
Lecture: Wednesdays 4:30-5:30 PM, Hollister B14
Discussion:
ED
Discussion
Grades:
CMS
Lecturers: Deniz Bölöni-Turgut, Jake Silver,
Audrey Wang, Mericel Tao, Srivatsa Kundurthy
Office Hours
| TA | Date and Time | Location |
| Cade Jin | Mon 10am-11am | Hollister 314 |
| Dan Wei Zuo | Mon 1pm-2pm | Hollister 368 |
| Srivatsa Kundurthy | Tue 3:30pm-4:30pm | Zoom |
| Jake Silver | Wed 1pm-2pm | Hollister 372 |
| Mericel Tao | Thu 12pm-1pm | Zoom |
| Theo Jeliazkov | Thu 3pm-4pm | Hollister 320 |
| Deniz Bölöni-Turgut | Fri 2:30pm-3:30pm | Phillips 307 |
Enrollment Information
Enrollment for the course on Student Center begins around the first day of the course (February 14th). Please contact the course manager for more information (add deadline for Spring 2024 is February 23rd).
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.
Overview
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.