Linear Statistical Models (SYS 4021/6021)

This course describes Evidence Informed Systems Engineering (EISE). The primary tools for EISE come from linear statistical models and the course demonstrates the use of these models for problem understanding, prediction, and control. Specific topics include multiple regression, principal components, analysis of covariance, logistic regression, time series analysis, bootstrapping, and response surfaces. Course lectures concentrate on the theory and practice of model construction, while projects provide open-ended problem solving situations that illustrate the broad applicability of the methods.

Machine Learning (SYS 6016 / CS 6316)

Machine learning is the study of how to build systems that can learn and adapt from experience. Machine learning is widely used in fields such as advertising, robotics, and medicine. Recent applications of machine learning are seen in self-driving cars, speech recognition, and recommender systems. A major focus in machine learning is the design and development of algorithms that recognize patterns in data and generalize in order to make an intelligent decision. This course provides an introduction to machine learning algorithms and applications. The primary focus of the course is supervised learning techniques with a culmination in unsupervised learning. Selected topics include Bayesian learning, artificial neural networks, evolutionary algorithms, reinforcement learning and instancebased learning. The course is project-oriented, with primary emphasis placed on programming assignments applying and evaluating learning algorithms.

System Design I & II (SYS 4053 & 4054)

A design project extending throughout the fall and spring semesters. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience.