CSCI 446 - Neural Networks for Machine Learning

Credits: (3)
Prerequisite(s): (MATH 106 or MATH 351) and MATH 211 and MATH 212
Machine learning enables computers to learn from experience. The first part of the course will cover foundational topics in pattern recognition and machine learning such as probability distributions, linear models for regression, and linear models for classification. The second part of the course will examine nonlinear models for regression and classification, focusing on artificial neural networks. Neural networks are being used to achieve state-of-the-art performance at applications in different modalities and domains. Students will be required to analyze and present relevant research papers.