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111 - 120 of 228 results for: MS

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

MS&E 237A: Bandit Learning: Behaviors and Applications (EE 277)

The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g., CS106B), probability (e.g., MS&E 121, EE 178 or CS 109), machine learning (e.g., EE 104/ CME 107, MS&E 226 or CS 229).
Terms: Aut | Units: 3

MS&E 237B: Reinforcement Learning: Behaviors and Applications (EE 370)

This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites: EE277.
Terms: Win | Units: 3

MS&E 238: Computational and Algorithmic Aspects of Fairness

Fairness means different things to different people, and in different contexts. This class discusses how fairness has been applied in settings where an algorithm is computing or mediating an outcome. Settings involve resource allocation by a centralized decision maker, fair division of a cake among agents using a game, market mechanisms for resource allocation, bail and credit decisions, social choice, fair allocation of costs, recommendation systems, recruitment, admissions, and computational advertising. In each setting, we describe commonly used fairness criteria, efficiency of computation, existence of solutions that match these criteria, and examples of how these criteria are deployed in practice. Prerequisites: MS&E 111 or CS 161; knowledge of game theory; knowledge of analysis. Students without prior knowledge of game theory will be required to complete a separate one week "introduction to game theory" online module during the first two weeks of class.

MS&E 239: Market Design in Action

This project-based experiential course is designed for advanced undergraduate and masters students familiar either with market design basics or machine learning methods who are interested in studying and potentially building a platform in a specific application domain. Applications of interest include the sharing economy, online advertising, blockchains and decentralized finance, as well as markets for allocating public goods. Prerequisites: 230, 260, CS 230, or equivalents.
Last offered: Spring 2022
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