Introduction to Machine Learning

Catalog description

This course will provide a theoretical and hands-on introduction to the basics of machinelearning and its application to various real-world problems. The course focuses on supervised learning problems including classification and regression. The course also discusses reinforcement learning. Unsupervised learning techniques such for dimension reduction and clustering will also be discussed. A wide range of different machine algorithms will be surveyed such as k-nearest-neighbors, polynomial curve fitting, logistic regression, support vector machines, decision trees, ensemble methods, and artificial neural networks. The course will also discuss ethical considerations in the application of machine learning. The course will be a feature a final project demonstrating mastery of the material.