Machine Learning as a field has grown considerably over the past few decades. In this course, we will explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as Pytorch. By the end of this course, you should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data.
- Course number
- CMSC B383
- Instructor
- Adam Poliak
- Teaching Assistants
- Course Staff
- Website
- https://cs.brynmawr.edu/cs383-ml/
- Discussion Forum
- Piazza
- Time and place
- Fall 2024, TTh 10:10-11:30am, Location: Park 159
- Lab W: 2:40-4:00pm, Location: Park 231
- Office Hours
- Times
- Prerequisites
- The following courses are required with a grade of 2.0 or better (or permission of
the instructor):
- CMSC B151 or CMSC HC 106/107
- MATH B203 or MATH H215
- CMSC 231
- Course Readings
- Each lecture has an accompanying chapter/section of the following free online textbooks:
- A Course in Machine Learning by Hal Duame III (“Duame” in schedule below)
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (“ISL” in schedule below)
- Some lectures will have accompanying optional reading related to the lecture’s topic
Grading
- Homeworks: 35%
- Labs: 5%
- Midterms: 40%
- Final Project: 15%
- Participation (includes quizzes): 5%
See the Policies for more details.