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41 - 50 of 162 results for: MS&E

MS&E 211: Introduction to Optimization (ENGR 62, MS&E 111)

Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.
Terms: Aut | Units: 3-4

MS&E 211DS: Introduction to Optimization: Data Science (MS&E 111DS)

Formulation and computational analysis of linear, discrete, and other optimization problems. Strong emphasis on data science and machine learning applications, as well as applications in matching and pricing in online markets. Prerequisite: CME 100 or MATH 51.
Terms: Win | Units: 3-4

MS&E 211X: Introduction to Optimization (Accelerated) (MS&E 111X)

Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.
Terms: Spr | Units: 3-4

MS&E 212: Graph and Combinatorial Optimization (MS&E 112)

Optimization problems dealing with graph structure. Topics: introduction to graph theory; combinatorial optimization problems on networks including network flows, matching, and assignment problems; NP-completeness and approximation algorithms; applications in the study of social networks, market design, and bioinformatics. Prerequisites: basic concepts in linear algebra, probability theory, CS 106A or X.
Terms: Win | Units: 3

MS&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)

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

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
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