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111 - 120 of 157 results for: MS&E

MS&E 332: Topics in Social Algorithms

In depth discussion of selected research topics in social algorithms, including networked markets, collective decision making, recommendation and reputation systems, prediction markets, social computing, and social choice theory. The class will include a theoretical project and a paper presentation. Prerequisites: CS 261 or equivalent; understanding of basic game theory.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 333: Social Algorithms

This seminar will introduce students to research in the field of social algorithms, including networked markets, collective decision making, recommendation and reputation systems, prediction markets, social choice theory, and models of influence and contagion.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

MS&E 334: Topics in Social Data

This course provides a in-depth survey of methods research for the analysis of large-scale social and behavioral data. There will be a particular focus on recent developments in discrete choice theory and preference learning. Connections will be made to graph-theoretic investigations common in the study of social networks. Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs. The course is intended for Ph.D. students, but masters students with an interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ugander, J. (PI)

MS&E 335: Queueing and Scheduling in Processing Networks

Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Stability analysis of queueing systems. Key results on single queues and queueing networks. Controlled queueing systems. Dynamic routing and scheduling in processing networks. Applications to modeling, analysis and performance engineering of computing systems, communication networks, flexible manufacturing, and service systems. Prerequisite: 221 or equivalent.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 336: Platform and Marketplace Design

The last decade has witnessed a meteoric rise in the number of online markets and platforms competing with traditional mechanisms of trade. Examples of such markets include online marketplaces for goods, such as eBay; online dating markets; markets for shared resources, such as Lyft, Uber, and Airbnb; and online labor markets. We will review recent research that aims to both understand and design such markets. Emphasis on mathematical modeling and methodology, with a view towards preparing Ph.D. students for research in this area. Prerequisites: Mathematical maturity; 300-level background in optimization and probability; prior exposure to game theory.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 338: Reinforcement Learning

Advanced material in this area is sometimes taught for the first time as a topics course. Prerequisite: consent of instructor.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Van Roy, B. (PI)

MS&E 347: Credit Risk: Modeling and Management

Credit risk modeling, valuation, and hedging emphasizing underlying economic, probabilistic, and statistical concepts. Point processes and their compensators. Structural, incomplete information and reduced form approaches. Single name products: corporate bonds, equity, equity options, credit and equity default swaps, forwards and swaptions. Multiname modeling: index and tranche swaps and options, collateralized debt obligations. Implementation, calibration and testing of models. Industry and market practice. Data and implementation driven group projects that focus on problems in the financial industry.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Giesecke, K. (PI)

MS&E 348: Optimization of Uncertainty and Applications in Finance

How to make optimal decisions in the presence of uncertainty, solution techniques for large-scale systems resulting from decision problems under uncertainty, and applications in finance. Decision trees, utility, two-stage and multi-stage decision problems, approaches to stochastic programming, model formulation; large-scale systems, Benders and Dantzig-Wolfe decomposition, Monte Carlo sampling and variance reduction techniques, risk management, portfolio optimization, asset-liability management, mortgage finance. Projects involving the practical application of optimization under uncertainty to financial planning.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Infanger, G. (PI)

MS&E 349: Financial Statistics

Topics in financial statistics with focus on current research: Time-series modeling, volatility modeling, high-frequency statistics, large-dimensional factor modeling and estimation of continuous-time processes. Prerequisites: 220, 226 or STATS 200, 221 or STATS 217, 245A, or equivalents.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Pelger, M. (PI)

MS&E 351: Dynamic Programming and Stochastic Control

Markov population decision chains in discrete and continuous time. Risk posture. Present value and Cesaro overtaking optimality. Optimal stopping. Successive approximation, policy improvement, and linear programming methods. Team decisions and stochastic programs; quadratic costs and certainty equivalents. Maximum principle. Controlled diffusions. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. MATLAB. Prerequisites: MATH 113, 115; Markov chains; linear programming.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit
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