Fall 2020 Elective Courses for ISyE Undergraduates
IE 5080—Reinforcement Learning and Dynamic Programming
Description: In recent years there have been many well publicized success stories of reinforcement learning, e.g., algorithms with superhuman performance in many challenging games Go, Chess, Dota and others. Reinforcement learning is a class of algorithms that are well suited for approximately solving large and challenging multistage decision problems. These problems can in principle be solved by dynamic programming, but are often so large that it is not computationally feasible to solve them exactly. In this course we will first discuss the basic dynamic programming approaches for solving these problems exactly, we will then discuss several approaches that can be used to achieve good approximate solutions.
Instructor: Kevin Leder
Time: Tuesdays and Thursdays from 1:25 PM to 3:20 PM
Credits: 4 credits
IE 5111—Systems Engineering I
Description: Systems engineering thinking/techniques are presented in 5111. Hands-on techniques are applied to specific problems. Topics are pertinent to effectiveness of design process. Covers practices and organizational/reward structure to support collaborative, globally distributed design teams.
Instructor: Andrew Fried
Time: Mondays from 6:10 PM to 8:00 PM
Credits: 2 credits
IE 5524—Process Transformation through Lean Tools
Description: Lean is a systematic methodology that improves processes by identifying and removing sources of waste in an organization. Lean tools, such as value stream mapping, Kaizen, kanban systems, visual systems, and 5S, improve processes by identifying and removing sources of waste. In this course, you will learn and utilize key Industrial Engineering methodologies to identify opportunities, prioritize these opportunities, develop solutions and create cost models of the solutions effectiveness. Applications of lean process improvement in areas such as manufacturing, healthcare, service operations, and business processes will be considered.
Instructor: Lisa Miller and Jeremiah Johnson
Time: Mondays from 4:00 PM to 5:45 PM
Credits: 2 credits
IE 5561—Analytics and Data-Driven Decision Making
Description: Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language.
Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
Instructor: Darin England
Time: Mondays and Wednesdays from 9:05 AM to 11:00 AM
Credits: 4 credits