Mach Learning for Engineering

Catalog description

Provide a broad introduction to machine learning and its applications. We will briefly review python programming, statistics, and linear algebra. Then, fundamentals of machine learning will be introduced, with topics including supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems), and fundamental theories (bias/variance theory). ECE applications such as time sequence forecasting, speech recognition, and image processing will be described. Prerequisites: ISE 261 & ECE 212, or equivalent with permission from instructor. Offered in Spring.