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MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each course once. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a statement showing relevance to degree program along with offer letter to the Student Services Office before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Course may be repeated for credit. To receive a permission code to enroll, please submit this form: https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.

Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.

This class will illustrate applications of optimization principles such as linear and non-linear programming, decision making under uncertainty, and dynamic programming in several important real-world scenarios, including Machine Learning, Market Design, Logistics and Revenue Management, Centralized and Decentralized Finance, Recommendation Systems, and Participatory Budgeting. The focus will be on applying the techniques, and in addition to the modeling, there will also be several hands-on assignments that will require you to deal with large and complex data sets. Prerequisites: Linear programming at the level of MS&E 111; proficiency in some programming language (preferably python).

Focus is on time-dependent random phenomena. Topics: discrete time Markov chains, Markov jump processes, queueing theory, and applications. Emphasis on model-building, computation, and related calibration and statistical issues. Prerequisite: 220 or equivalent, or consent of instructor.

Discrete-event systems, generation of uniform and non-uniform random numbers, Monte Carlo methods, programming techniques for simulation, statistical analysis of simulation output, efficiency-improvement techniques, decision making using simulation, applications to systems in computer science, engineering, finance, and operations research. Prerequisites: working knowledge of a programming language such as C, C++, Java, Python, or FORTRAN; calculus-base probability; and basic statistical methods.

The course will explore applied topics at the intersection of game theory, data science and artificial intelligence. The first part of the course will focus on computational approaches to solving complex games, with applications in developing successful algorithmic agents and explore recent successes in the games of Go, Stratego, Poker and Diplomacy. The lectures will provide the foundations of the methods that underlie these computational game theory methods (rooted in the theory of learning in games) and the assignments will explore implementation of simple variants. The second part of the course will explore the interplay between data science and mechanism design. We will overview topics such as optimizing auctions and mechanisms from data and explore applications in optimizing online auction markets. We will also overview methodologies for learning structural parameters in games and econometrics in games and how these can be used to analyze data that stem from strategic interactions, such as auction data. The third part of the course will explore topics that relate to deploying machine learning and data science pipelines in the presence of strategic behavior. Topics will include A/B testing in markets, with applications to A/B testing on digital platforms such as Uber, Amazon and other matching platforms.