Units:

12

Description:

This course provides an introduction to machine learning with a special focus on engineering applications. The course starts with a mathematical background required for machine learning and covers approaches for supervised learning (linear models, kernel methods, decision trees, neural networks) and unsupervised learning (clustering, dimensionality reduction), as well as theoretical foundations of machine learning (learning theory, optimization). Evaluation will consist of mathematical problem sets and programming projects targeting real-world engineering applications. This course is crosslisted with 18-661. Although students in 18-461 will share lectures with students in 18-661, students in 18-461 will receive distinct homework assignments, distinct programming projects, and distinct exams from the ones given to students in 18-661. Specifically, the homework assignments, programming projects, and exams that are given to the 18-661 students will be more challenging than those given to the 18-461 students.

Prerequisites: 18-202 and 21-127 and 15-122 and (21325 or 36225 or 36218 or 36217 or 36219) or equivalent

Last Modified: 2024-11-20 11:29AM

Semesters offered:

• Spring 2025
• Fall 2024
• Spring 2024
• Fall 2023
• Spring 2023
• Fall 2022
• Spring 2022
• Fall 2021
• Spring 2021
• Fall 2020
• Spring 2020
• Fall 2019
• Spring 2019
• Fall 2018