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141 - 150 of 206 results for: MS

MS&E 326: Advanced Topics in Game Theory with Engineering Applications

Advanced Topics in Game Theory with Engineering Applications
| Repeatable for credit (up to 99 units total)

MS&E 327: Topics in Causal Inference (STATS 209A)

This course introduces the fundamental ideas and methods in causal inference, and surveys a broad range of problems and applications. Emphasis will be on framing causal problems and identifying causal effects in both randomized experiments and observational studies. Topics will include: the potential outcomes framework; randomization-based inference and covariate adjustment; matching, and IPW; instrumental variables, regression discontinuity and synthetic ncontrols. Examples and applications will be taken from the fields of education, political science, economics, public health and digital marketing.
Terms: Aut | Units: 3

MS&E 330: Law, Order, & Algorithms (CS 209, CSRE 230, SOC 279)

Human decision making is increasingly being displaced by predictive algorithms. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises. Admission is by consent of instructor and is limited to 20 students. To enroll in the class, please complete the course application by March 20, available at: https://5harad.com/mse330/. Grading is based on response papers, class participation, and a final project. Prerequisite: CS 106A or equivalent knowledge of coding.
Terms: Spr | Units: 3
Instructors: Goel, S. (PI)

MS&E 332: Security and Risk in Computer Networks

Risk management of large scale computing and networking systems with respect to security, data integrity, performance collapse, and service disruption. Qualitative and analytical basis for assessment, modeling, control, and mitigation of network risks. Stochastic risk models. Contact process. Random fields on networks. Virus and worm propagation dynamics and containment. Denial of service attacks. Intruder detection technologies. Distributed network attacks and countermeasures. Disaster recovery networks. Network protection services and resource placement. Autonomic self-defending networks. Economics of risk management. Emphasis is on analytics and quantitative methods.
Terms: Win | Units: 3
Instructors: Bambos, N. (PI)

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, rank aggregation, centrality and ranking on graphs, and random graphs. The course is intended for Ph.D. students, but masters students interested in research topics are welcome. Recommended: 221, 226, CS161, or equivalents.
Last offered: Autumn 2018

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: Aut | Units: 3
Instructors: Bambos, N. (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.
Last offered: Winter 2020

MS&E 337: Network Structure and Epidemics (MS&E 235)

Explores the underlying network structure of social, economic, and technological world using techniques from graph theory and economics, as well as machine learning and data analysis. Prerequisite: 226, CME 195, or equivalents. Recommended: 212.
Terms: Aut | Units: 3

MS&E 338: Reinforcement Learning: Frontiers

This class covers subjects of contemporary research contributing to the design of reinforcement learning agents that can operate effectively across a broad range of environments. Topics include exploration, generalization, credit assignment, and state and temporal abstraction. An important component of the class is a research project aimed at understanding a focused issue in reinforcement learning. Can be repeated for credit. Prerequisites: 226, CS 234, or EE 277, and experience with mathematical proofs.
Terms: Spr | Units: 3 | Repeatable 4 times (up to 12 units total)
Instructors: Van Roy, B. (PI)

MS&E 346: Reinforcement Learning for Stochastic Control Problems in Finance (CME 241)

This course will explore a few problems in Mathematical Finance through the lens of Stochastic Control, such as Portfolio Management, Derivatives Pricing/Hedging and Order Execution. For each of these problems, we formulate a suitable Markov Decision Process (MDP), develop Dynamic Programming (DP) solutions, and explore Reinforcement Learning (RL) algorithms. The course emphasizes the theory of DP/RL as well as modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of coding exercises of the methods. No pre-requisite coursework expected, but a foundation in undergraduate Probability, basic familiarity with Finance, and Python coding skills are required. Dynamic Programming or Reinforcement Learning background not required.
Terms: Win | Units: 3
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