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51 - 60 of 162 results for: MS&E

MS&E 224: Resilience and Reliable Network Design

Planning for large-scale infrastructure networks is with the objective of improving reliability and resilience. Over the last decades, a number of disasters have resulted in substantial losses of human, significant damage to property, and massive service interruptions for a number large infrastructure systems. The concepts reliability and resilience are now frequently used to characterize how well these infrastructure systems, their operators, and their users are prepared and capable to recover from disruptive events. In order to analyze a network, attention must be paid to three important aspects: First, the election of a failure model that is complex enough to capture the interaction between components but, at the same time, simple enough to calibrate with the available information. Second, study the performance of the network. This means that given a failure model, we need to develop methodologies to compute the performance of the network. Finally, the comparison of different networ more »
Planning for large-scale infrastructure networks is with the objective of improving reliability and resilience. Over the last decades, a number of disasters have resulted in substantial losses of human, significant damage to property, and massive service interruptions for a number large infrastructure systems. The concepts reliability and resilience are now frequently used to characterize how well these infrastructure systems, their operators, and their users are prepared and capable to recover from disruptive events. In order to analyze a network, attention must be paid to three important aspects: First, the election of a failure model that is complex enough to capture the interaction between components but, at the same time, simple enough to calibrate with the available information. Second, study the performance of the network. This means that given a failure model, we need to develop methodologies to compute the performance of the network. Finally, the comparison of different network designs and to choose, according to a budget constraint, those which have a better performance. Natural disasters, such as earthquake and wildfires, can cause large blackouts and pose a challenge that a network should be able to overcome. Research into natural disaster impact on electric power systems is can help us understand the causes of the blackouts, explore ways to prepare and harden the grid, and increase the resilience of the power grid under such events. Discussion of how network design should address these challenges. Lectures, with regular weekly assignments and a study group for a final project. The target students include graduate and undergraduate students in MS&E, and other students on campus, including, for example, Civil and Environmental Engineering, ICME, and interested students at the Doerr School, among others. Prerequisite: probability such as 120, 220, or CEE 203. Recommended 121 or 221.
Terms: Win | Units: 3

MS&E 226: Fundamentals of Data Science: Prediction, Inference, Causality

This course is about understanding "small data": these are datasets that allow interaction, visualization, exploration, and analysis on a local machine. The material provides an introduction to applied data analysis, with an emphasis on providing a conceptual framework for thinking about data from both statistical and machine learning perspectives. Topics will be drawn from the following list, depending on time constraints and class interest: approaches to data analysis: statistics (frequentist, Bayesian) and machine learning; binary classification; regression; bootstrapping; causal inference and experimental design; multiple hypothesis testing. Class lectures will be supplemented by data-driven problem sets and a project. Prerequisites: CME 100 or MATH 51; 120, 220 or STATS 116; experience with R at the level of CME/ STATS 195 or equivalent.
Terms: Aut | Units: 3

MS&E 228: Applied Causal Inference with Machine Learning and AI (CS 288)

Fundamentals of modern applied causal inference. Basic principles of causal inference and machine learning and how the two can be combined in practice to deliver causal insights and policy implications in real world datasets, allowing for high-dimensionality and flexible estimation. Lectures will provide foundations of these new methodologies and the course assignments will involve real world data (from social science, tech industry and healthcare applications) and synthetic data analysis based on these methodologies. Prerequisites: basic knowledge of probability and statistics. Recommended: 226 or equivalent.
Terms: Win | Units: 3

MS&E 230: Market Design for Engineers

Markets are everywhere around us but don't always achieve desired goals. Market failures occur due to a variety of frictions and need design to be fixed. The design of marketplace varies depending on the type of goods and possible transactions. This course will cover methods and classic results to analyze the behavior of a marketplace, whether it is successful and how to fix it, building especially on game theoretic tools. The course will further explore the trade-offs between efficiency and equitable outcomes and how to reach desired outcomes. Applications include matching students to schools, college admissions and the failure the desire to balance equity and merit, assigning vaccines, assigning interns to hospitals, assigning organs to patients, auction designs and pricing, information design, online platforms, allocation of food, transportation, and emissions. The course is intended for undergraduates, masters, but also PhD students who are interested in exposure to market design. Prerequisites: basic mathematical maturity at the level of Math 51, and probability at the level of MS&E 120, 220 or EE 178. Limited enrollment.
Last offered: Spring 2023

MS&E 231: Social Algorithms

Learning algorithms play increasingly central roles within modern complex social systems. In this course, we examine the design and behavior of algorithms in such contexts, including search algorithms, content recommendation systems, social recommendation algorithms, feed ranking algorithms, content moderation algorithms, and more. The course has a split focus on the technical design of such algorithms, as well the literature on theoretical and empirical evaluations in the presence of network effects, strategic behavior, and algorithmic confounding. Prerequisites: training in applied statistics at the level of MS&E 125 or above, including experience coding in Python.
Terms: Aut | Units: 3

MS&E 232: Introduction to Game Theory

Examines foundations of strategic environments with a focus on game theoretic analysis. Provides a solid background to game theory as well as topics in behavioral game theory and the design of marketplaces. Introduction to analytic tools to model and analyze strategic interactions as well as engineer the incentives and rules in marketplaces to obtain desired outcomes. Technical material includes non-cooperative and cooperative games, behavioral game theory, equilibrium analysis, repeated games, social choice, mechanism and auction design, and matching markets. Exposure to a wide range of applications. Lectures, presentations, and discussion. Prerequisites: basic mathematical maturity at the level of Math 51, and probability at the level of MS&E 120 or EE 178.
Terms: Win | Units: 3

MS&E 232H: Introduction to Game Theory (Accelerated)

Game theory uses mathematical models to study strategic interactions and situations of conflict and cooperation between rational decision-makers. This course provides an accelerated introduction to tools, models and computation in non-cooperative and cooperative game theory. Technical material includes normal and extensive form games, zero-sum games, Nash equilibrium and other solution concepts, repeated games, games with incomplete information, auctions and mechanism design, the core, and Shapley value. Exploration of applications of this material through playing stylized in-class and class-wide games and analyzing real-life applications. Prerequisites: mathematical maturity at the level of MATH51, and probability at the level of MS&E 120, or equivalent.
Terms: Win | Units: 3

MS&E 233: Game Theory, Data Science and AI

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 interaction more »
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.
Terms: Spr | Units: 3

MS&E 234: Data Privacy and Ethics

This course engages with ethical challenges in the modern practice of data science. The three main focuses are data privacy, personalization and targeting algorithms, and online experimentation. The focus on privacy raises both practical and theoretical considerations. As part of the module on experimentation, students are required to complete the Stanford IRB training for social and behavioral research. The course assumes a strong technical familiarity with the practice of machine learning and and data science. Limited enrollment. Recommended: 221, 226, CS 161, or equivalents.
Last offered: Winter 2022

MS&E 236: Machine Learning for Discrete Optimization (CS 225)

Machine learning has become a powerful tool for discrete optimization. This is because, in practice, we often have ample data about the application domain?data that can be used to optimize algorithmic performance, ranging from runtime to solution quality. This course covers how machine learning can be used within the discrete optimization pipeline from many perspectives, including how to design novel combinatorial algorithms with machine-learned modules and configure existing algorithms? parameters to optimize performance. Topics will include both applied machinery (such as graph neural networks, reinforcement learning, transformers, and LLMs) as well as theoretical tools for providing provable guarantees.
Terms: Spr | Units: 3
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