Introduction to Machine Learning, Fall 2022
|Vincent Fong & Neha Kulshreshtha||Mondays 5:30-6:30pm||Hollister 306|
|Kevin Jiang & Eric Guo||Tuesdays 2:30-3:30pm||Hollister 368|
|Emily Weed & Everett Lee||Tuesdays 4:30-5:30pm||Hollister 368|
|Varun Gande & Vivian Chen||Wednesdays 3-4pm||Rhodes 590|
|Jacob Mayourian & Brendan Lo||Thursdays 3-4pm||Rhodes 400|
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.