MS&E 211X: Introduction to Optimization (Accelerated) (ENGR 62X, 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 interiorpoint, gradient, Newton, and barrier. Prerequisite:
CME 100 or
MATH 51 or equivalent.
Terms: Aut, Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Saberi, A. (PI)
;
Ye, Y. (PI)
;
Bartan, B. (TA)
;
Hinder, O. (TA)
;
Kollagunta Krishnaswamy, A. (TA)
;
Merrick, J. (TA)
;
Sakshuwong, S. (TA)
;
Song, L. (TA)
;
Wu, C. (TA)
MS&E 212: Mathematical Programming and Combinatorial Optimization (MS&E 112)
Combinatorial and mathematical programming (integer and nonlinear) techniques for optimization. Topics: linear program duality and LP solvers; integer programming; combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows; matching and assignment problems; dynamic programming; linear approximations to convex programs; NPcompleteness. Handson exercises. Prerequisites: 111 or
MATH 103,
CS 106A or X.
Terms: not given this year, last offered Winter 2017

Units: 3

Grading: Letter or Credit/No Credit
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 firstorder methods for both smooth and nonsmooth 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.
Terms: Spr

Units: 3

Grading: Letter (ABCD/NP)
MS&E 220: Probabilistic Analysis
Concepts and tools for the analysis of problems under uncertainty, focusing on model building and communication: the structuring, processing, and presentation of probabilistic information. Examples from legal, social, medical, and physical problems. Spreadsheets illustrate and solve problems as a complement to analytical closedform solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite: multivariable calculus and linear algebra. Recommended: knowledge of spreadsheets.
Terms: Aut, Sum

Units: 34

Grading: Letter or Credit/No Credit
MS&E 221: Stochastic Modeling
Focus is on timedependent random phenomena. Topics: discrete and continuous time Markov chains, renewal processes, queueing theory, and applications. Emphasis is on building a framework to formulate and analyze probabilistic systems. Prerequisite: 220 or equivalent, or consent of instructor.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
MS&E 223: Simulation
Discreteevent systems, generation of uniform and nonuniform random numbers, Monte Carlo methods, programming techniques for simulation, statistical analysis of simulation output, efficiencyimprovement 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; calculusbase probability; and basic statistical methods.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Blanchet Mancilla, J. (PI)
;
Zhang, T. (TA)
MS&E 226: "Small" Data
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 datadriven 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

Grading: Letter or Credit/No Credit
Instructors:
Johari, R. (PI)
;
Choi, J. (TA)
;
Ragain, S. (TA)
;
Samaniego de la Fuente, S. (TA)
;
Schmit, S. (TA)
;
Walsh, D. (TA)
;
Zhang, T. (TA)
MS&E 231: Introduction to Computational Social Science (SOC 278)
With a vast amount of data now collected on our online and offline actions  from what we buy, to where we travel, to who we interact with  we have an unprecedented opportunity to study complex social systems. This opportunity, however, comes with scientific, engineering, and ethical challenges. In this handson course, we develop ideas from computer science and statistics to address problems in sociology, economics, political science, and beyond. We cover techniques for collecting and parsing data, methods for largescale machine learning, and principles for effectively communicating results. To see how these techniques are applied in practice, we discuss recent research findings in a variety of areas. Prerequisites: introductory course in applied statistics, and experience coding in R, Python, or another highlevel language.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
MS&E 233: Networked Markets
An introduction to economic analysis for modern online services and systems. Topics include: Examples of networked markets. Online advertising. Recommendation and reputation systems. Pricing digital media. Network effects and network externalities. Social learning and herd behavior. Markets and information. Prerequisites:
CME 100 or
Math 51, and probability at the level of MS&E 220 or equivalent. No prior economics background will be assumed; requisite concepts will be introduced as needed.
Terms: not given this year, last offered Spring 2014

Units: 3

Grading: Letter or Credit/No Credit
MS&E 234: Data Privacy and Ethics
This course engages with difficult 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 will raise both practical and theoretical considerations. As part of the module on experimentation, students will be required to complete the Stanford IRB training for social and behavioral research. The course will assume a strong familiarity with the practice of machine learning and and data science. Recommended: MS&E 226, MS&E 231,
CS 229, or equivalents.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Ugander, J. (PI)
;
Arrieta Ibarra, I. (TA)
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