21 - 30 of 55 results for: MS&E

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.

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.

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.

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.

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.

Principal methods of economic analysis of the production activities of firms, including production technologies, cost and profit, and perfect and imperfect competition; individual choice, including preferences and demand; and the market-based system, including price formation, efficiency, and welfare. Practical applications of the methods presented. Recommended: 111 or 211, and ECON 50.

Practical introduction to financial risk analytics. The focus is on data-driven modeling, computation, and statistical estimation of credit and market risks. Case studies based on real data will be emphasized throughout the course. Topics include mortgage risk, asset-backed securities, commercial lending, consumer delinquencies, online lending, derivatives risk. Tools from machine learning and statistics will be developed. Data sources will be discussed. The course is intended to enable students to design and implement risk analytics tools in practice. Prerequisites: MS&E 245A or similar, some background in probability and statistics, working knowledge of R, Matlab, or similar computational/statistical package.

Key functions of finance in both large and small companies, and the core concepts and key analytic tools that provide their foundation. Making financing decisions, evaluating investments, and managing cashflow, profitability and risk. Designing performance metrics to effectively measure and align the activities of functional groups and individuals within the firm. Structuring relationships with key customers, partners and suppliers. Limited enrollment. Recommended: 145, 245A, or equivalent.

Techniques of analysis of risk management decisions in engineering and other systems involving preferences and trade-offs (technical, human, environmental aspects). Elements of decision analysis; probabilistic risk analysis in the public or private sector (fault trees, event trees, systems dynamics); Bayesian updating and learning (elementary notions of quantum computing for complex cases); value of tests, economic analysis of failure consequences (human safety and long-term economic discounting); case studies such as space systems, nuclear power plants, medical systems and cyber security. Pre-requisites: probability, stochastic processes, and convex optimization.