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111 - 120 of 168 results for: MS&E

MS&E 325: Advanced Topics in Applied Probability

Current stochastic models, motivated by a wide range of applications in engineering, business, and science, as well as the design and analysis of associated computational methods for performance analysis and control of such stochastic systems.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 330: Law, Order & Algorithms (SOC 279)

Data and algorithms are rapidly transforming law enforcement and criminal justice, including how police officers are deployed, how discrimination is detected, and how sentencing, probation, and parole terms are set. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. In this course, we analyze recent court decisions, discuss methods from machine learning and game theory, and examine the often subtle relationship between law, public policy, and statistics. The class is centered around several data-intensive projects in criminal justice that students work on in interdisciplinary teams. Students work closely with criminal justice agencies to carry out these projects, with the goal of producing research that impacts policy. Students with a background in statistics, computer science, law, and/or public policy are encouraged to participate. Enrollment is limited, and project teams will be selected during the first week of class.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

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: The Structure of Social Data

This course provides a survey of recent research in the study of social networks and large-scale social and behavioral data. Topics will include network models based on random graphs and their properties; centrality and ranking on graphs; ranking from comparisons; heavy-tailed statistical distributions for social data; the wisdom of crowds; homophily and social influence; experimentation and causal inference on networks. Prerequisites: 221, 226, CS161.
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: Advanced Topics in Information Science and Technology

Advanced material in this area is sometimes taught for the first time as a topics course. Prerequisite: consent of instructor.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

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: alternate years, given next year | Units: 3 | Grading: Letter or Credit/No Credit
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