An introduction to the practice of management engineering, including topics such as criteria-based decision making with a focus on efficiency and effectiveness; cause and effect analysis, and an engineering approach to problem solving; flow and process analysis; teamwork and project management; decision-making tools; and written and verbal communication. Engineering design methods are also introduced, including a design project with small groups, as are aspects of the engineering profession including topics such as ethics, safety, and legal liability. Professional development for preparation for co-op terms is also included.

This course introduces students to first and second order ordinary differential equations, vector calculus, and numerical methods for solution of systems of equations, and ordinary differential equations. Applications in management engineering are emphasized.

This course provides an introduction to machine learning, including supervised and unsupervised learning. Emphasis is placed on proper procedures for the training and testing of models. Topics covered may include data cleaning and transformation, overfitting and generalization, n-fold cross validation, regression, decision trees, neural networks, rule finding, and clustering. Students learn to apply machine learning methods to management engineering problems using common tools such as R and Python.

This course provides an introduction to the field of machine learning, with an emphasis on tools and algorithms that learn patterns from data. Topics covered may include data preparation, descriptive learning such as clustering, and predictive learning using classical methods such as regression and data-intensive methods such as deep learning. Students will read and present papers and complete a research project.