printer friendly pageMS&E 208D: Practical Training
MS&E students obtain employment in a relevant industrial or research activity to enhance professional experience, consistent with the degree program they are pursuing. Students submit a one-page statement showing relevance to degree program along with offer letter before the start of the quarter, and a 2-3 page final report documenting the work done and relevance to degree program at the conclusion of the quarter. Students may take each of A, B, and C once, and may petition to take D.
Terms: Aut, Win, Spr
| Units: 1
Instructors:
Giesecke, K. (PI)
;
Shachter, R. (PI)
MS&E 211: Linear and Nonlinear Optimization
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.
Terms: Aut
| Units: 3-4
Instructors:
Ye, Y. (PI)
;
Boucher, E. (TA)
;
Hinder, O. (TA)
;
Li, X. (TA)
;
Makhijani, R. (TA)
;
Wu, Q. (TA)
MS&E 212: Mathematical Programming and Combinatorial Optimization (MS&E 112)
Combinatorial and mathematical programming (integer and non-linear) 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; NP-completeness. Hands-on exercises. Prerequisites: 111 or
MATH 103,
CS 106A or X.
Terms: Win
| Units: 3
Instructors:
Saberi, A. (PI)
;
Skochdopole, N. (TA)
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 closed-form solutions. Topics: axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite:
CME 100 or
MATH 51. Recommended: knowledge of spreadsheets.
Terms: Aut
| Units: 3-4
MS&E 221: Stochastic Modeling
Focus is on time-dependent 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 consent of instructor.
Terms: Win
| 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
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 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
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 hands-on 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 large-scale 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 high-level language.
Terms: Aut
| Units: 3
Instructors:
Goel, S. (PI)
;
Arrieta Ibarra, I. (TA)
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.
Last offered: Spring 2014
MS&E 235: Analytics in Action
Examines the role of analytics in real-world solutions across different industries. Provides a short introduction on the main concepts of analytics, and addresses common modeling approaches for both supervised (e.g., regression and classification) and unsupervised techniques (e.g., clustering, anomaly detection and pattern recognition), using platforms such as Hadoop and R. Discussion of implementations of these models in various industries, such as manufacturing, retail, banking, marketing, telecom and security. Teams of students will be required to prepare and present an analytics use case, covering aspects related to data collection, pre-processing, modeling, analyses, visualization, recommendations, implementation, business value and ROI. Students will be expected to come prepared to class, ready to discuss the case at hand, and offer their thoughts and insights. Cases will be presented in the context of leading a data science team, much as a Chief Analytics Officer (CAO) would be expected to do. Prerequisite: 226,
CME 195, or equivalents.
Terms: Win
| Units: 3
Instructors:
Ben Gal, I. (PI)
;
Makhijani, R. (TA)
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