18-661   Introduction to Machine Learning for Engineers

Location: Pittsburgh

Units: 12

Semester Offered: Fall, Spring

Course 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.

See the original course description for the most recent information.


Carlee Joe-Wong
Guannan Qu
Yuejie Chi
Gauri Joshi