printer friendly pageMS&E 213: Introduction to Optimization Theory (CS 269O)
Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization. In particular, focus will be on first-order methods for both smooth and non-smooth convex function minimization as well as methods for structured convex function minimization, discussing algorithms such as gradient descent, accelerated gradient descent, mirror descent, Newton's method, interior point methods, and more. Prerequisite: multivariable calculus and linear algebra.
Last offered: Autumn 2020
MS&E 214: Advanced Applied Optimization
This class will illustrate applications of optimization principles such as linear and non-linear programming, decision making under uncertainty, and dynamic programming in several important real-world scenarios, including Machine Learning, Market Design, Logistics and Revenue Management, Centralized and Decentralized Finance, Recommendation Systems, and Participatory Budgeting. The focus will be on applying the techniques, and in addition to the modeling, there will also be several hands-on assignments that will require you to deal with large and complex data sets. Prerequisites: Linear programming at the level of MS&E 111; proficiency in some programming language (preferably python).
Terms: Spr
| Units: 3
MS&E 218: Applied Data Science (CME 218)
This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics. Students apply a computational and data analytics lens and use design thinking methodology. The course exposes students to important techniques in applied data science as well as to the soft skills necessary for success in applied data science, such as ethics, unintended consequences and team building. Enrollment by application only. Graduate students only. The course application closes Sept 25, 2023. Application and more information:
https://forms.gle/gzGXkJmGMVYuJabK7
Terms: Aut
| Units: 3
| Repeatable
2 times
(up to 6 units total)
Instructors:
Hanson, K. (PI)
;
Udell, M. (PI)
MS&E 220: Probabilistic Analysis
Concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: multivariable calculus and some linear algebra.
Terms: Aut
| Units: 3-4
Instructors:
Shachter, R. (PI)
;
Arpasova, K. (TA)
;
Rodriguez Silva Santisteban, F. (TA)
...
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Instructors:
Shachter, R. (PI)
;
Arpasova, K. (TA)
;
Rodriguez Silva Santisteban, F. (TA)
;
Xu, W. (TA)
;
Zhao, Y. (TA)
MS&E 221: Stochastic Modeling
Focus is on time-dependent random phenomena. Topics: discrete time Markov chains, Markov jump processes, queueing theory, and applications. Emphasis on model-building, computation, and related calibration and statistical issues. Prerequisite: 220 or equivalent, or consent of instructor.
Terms: Spr
| Units: 3
MS&E 223: Simulation
Discrete-event systems, generation of uniform and non-uniform random numbers, Monte Carlo methods, programming techniques for simulation, statistical analysis of simulation output, efficiency-improvement techniques, decision making using simulation, applications to systems in computer science, engineering, finance, and operations research. Prerequisites: working knowledge of a programming language such as C, C++, Java, Python, or FORTRAN; calculus-base probability; and basic statistical methods.
Terms: Spr
| Units: 3
Instructors:
Blanchet, J. (PI)
;
Lotidis, K. (TA)
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
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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
Instructors:
Barrera Martinez, J. (PI)
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
Instructors:
Johari, R. (PI)
;
Boissier, A. (TA)
;
Elfarsdottir, A. (TA)
...
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Instructors:
Johari, R. (PI)
;
Boissier, A. (TA)
;
Elfarsdottir, A. (TA)
;
Sojitra, R. (TA)
;
Thapa, I. (TA)
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
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