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41 - 50 of 55 results for: MS&E

MS&E 312: Optimization Algorithms (CME 334, CS 369O)

Fundamental theory for solving continuous optimization problems with provable efficiency guarantees. Coverage of both canonical optimization methods and techniques, e.g. gradient descent, mirror descent, stochastic methods, acceleration, higher-order methods, etc. and canonical optimization problems, critical point computation for non-convex functions, smooth-convex function minimization, regression, linear programming, etc. Focus on provable rates for solving broad classes of prevalent problems including both classic problems and those motivated by large-scale computational concerns. Discussion of computational ramifications, fundamental information-theoretic limits, and problem structure. Prerequisite: linear algebra, multivariable calculus, probability, and proofs.
Terms: Win | Units: 3

MS&E 314: Optimization in Data Science and Machine Learning

Optimization in Data Science and Machine Learning
Terms: Win | Units: 3
Instructors: Ye, Y. (PI)

MS&E 323: Stochastic Simulation

Emphasis is on the theoretical foundations of simulation methodology. Generation of uniform and non-uniform random variables. Discrete-event simulation and generalized semi-Markov processes. Output analysis (autoregressive, regenerative, spectral, and stationary times series methods). Variance reduction techniques (antithetic variables, common random numbers, control variables, discrete-time, conversion, importance sampling). Stochastic optimization (likelihood ratio method, perturbation analysis, stochastic approximation). Simulation in a parallel environment. Prerequisite: MS&E 221 or equivalent.
Terms: Win | Units: 3
Instructors: Glynn, P. (PI)

MS&E 336: Computational Social Choice (CS 366)

An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Prerequisites: algorithms at the level of 212 or CS 161, probability at the level of 221, and basic game theory, or consent of instructor.
Terms: Win | Units: 3
Instructors: Goel, A. (PI)

MS&E 346: Foundations of Reinforcement Learning with Applications in Finance (CME 241)

This course is taught in 3 modules - (1) Markov Processes and Planning Algorithms, including Approximate Dynamic Programming (3 weeks), (2) Financial Trading problems cast as Stochastic Control, from the fields of Portfolio Management, Derivatives Pricing/Hedging, Order-Book Trading (2 weeks), and (3) Reinforcement Learning Algorithms, including Monte-Carlo, Temporal-Difference, Batch RL, Policy Gradient (4 weeks). The final week will cover practical aspects of RL in the industry, including an industry guest speaker. The course emphasizes the theory of RL, modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of programming exercises. No pre-requisite coursework expected, but a foundation in undergraduate Probability, basic familiarity with Finance, and Python programming skills are required.
Terms: Win | Units: 3

MS&E 365: Topics in Market Design (ECON 287)

Market design is a field that links the rules of the of the marketplace to understand frictions, externalities and more generally economic outcomes. The course provides theoretical foundations on assignment and matching mechanisms as well as mechanism design. Emphasis on theories at the intersection of economics, CS and operations as well as applications that arise in labor markets, organ allocation, platforms. Exposes students to timely market design challenges. Guest lectures and a research project. The class offers an opportunity to begin a research project. Students read and critique papers and write and present a final paper.
Terms: Win | Units: 3 | Repeatable for credit
Instructors: Ashlagi, I. (PI)

MS&E 371: Innovation and Strategic Change

Doctoral research seminar, limited to Ph.D. students. Current research on innovation strategy. Topics: scientific discovery, innovation search, organizational learning, evolutionary approaches, and incremental and radical change. Topics change yearly. Recommended: course in statistics or research methods.
Terms: Win | Units: 1-3 | Repeatable for credit
Instructors: Katila, R. (PI)

MS&E 385: Doctoral Seminar in Race and Ethnicity

What is the difference between race and ethnicity? In what ways can we theorize the difference (if it exists)? How does modern racism work? And how does immigration change a nation's racial landscape? This graduate course surveys classic and contemporary writings on race and ethnicity mainly within the sociological tradition. We begin with Weber and some non-canonized classics, including the works of W.E.B. DuBois and Franz Fannon to understand how the study of race and ethnicity emerged in Social Science as a contrast to the biological determinist scholarship of the time. We pay attention to the way that social scientists emphasized the role of culture, structure, and status. From there we proceed to examine the more contemporary arguments, including uncovering the various mechanisms that undergird the (re)production or transformation of racial and ethnic boundaries. We spend time examining the literature on inequality and questions about the significance of race and racism. In additi more »
What is the difference between race and ethnicity? In what ways can we theorize the difference (if it exists)? How does modern racism work? And how does immigration change a nation's racial landscape? This graduate course surveys classic and contemporary writings on race and ethnicity mainly within the sociological tradition. We begin with Weber and some non-canonized classics, including the works of W.E.B. DuBois and Franz Fannon to understand how the study of race and ethnicity emerged in Social Science as a contrast to the biological determinist scholarship of the time. We pay attention to the way that social scientists emphasized the role of culture, structure, and status. From there we proceed to examine the more contemporary arguments, including uncovering the various mechanisms that undergird the (re)production or transformation of racial and ethnic boundaries. We spend time examining the literature on inequality and questions about the significance of race and racism. In addition, we assess how assimilation and racialization developed over time. We then spend time thinking about how to consider race and ethnicity in research designs. Finally, we end with looking towards the future, including how technology is changing modern conceptualizations of race and the potential of policy to mitigate the effects of systemic racism.
Terms: Win | Units: 1-3
Instructors: Sheares, A. (PI)

MS&E 390: Doctoral Research Seminar in Health Systems Modeling (HRP 390)

Restricted to PhD students, or by consent of instructor. Doctoral research seminar covering current topics in health policy, health systems modeling, and health innovation. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-3 | Repeatable for credit
Instructors: Brandeau, M. (PI)

MS&E 408: Directed Reading and Research

Directed reading and research on a subject of mutual interest to student and faculty member. Available to undergraduate, master, and doctoral students. Student must clarify deliverables, units, and grading basis with faculty member before applicable deadlines. Prerequisite: consent of instructor
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
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