18-461: Introduction to Machine Learning for Engineers
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. ECE graduate students will be prioritized for 18-661, and ECE undergraduate students will be prioritized for 18-461. 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.
Anti-requisites: 10-401, 10-601, 10-701
Last Modified: 2020-12-01 10:51AM
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