EECE 429X - Machine Learning

Credits: 3

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. Offered Spring 2023 Prerequisites: ISE 261 & MATH 324, permission from instructor