MATH 400 Introduction to Machine Learning

This course introduces a variety of supervised and unsupervised machine learning algorithms. Students will study methods such as regression and classification, decision trees, naive Bayes, Principal Component Analysis, support vector machines, neural networks, and unsupervised learning methods. A brief introduction to Deep Learning will also be included. Students will gain hands-on experience applying machine learning techniques to real-world data from multiple disciplines such as social sciences, life sciences, physical sciences, economics, education, and engineering.