2013-2014 2014-2015 2015-2016 2016-2017 2017-2018
Browse
by subject...
    Schedule
view...
 

11 - 20 of 39 results for: MS&E

MS&E 208C: 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 statement showing relevance to degree program along with offer letter to the Student Services Office 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 course once. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Brandeau, M. (PI)

MS&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 statement showing relevance to degree program along with offer letter to the Student Services Office 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 course once. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Brandeau, M. (PI)

MS&E 208E: Part-Time 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 statement showing relevance to degree program along with offer letter to the Student Services Office 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. Course may be repeated for credit. To receive a permission code to enroll, please submit this form: http://web.stanford.edu/~lcottle/forms/CPTapp.fb with statement and offer letter.
Terms: Aut, Win, Spr, Sum | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Brandeau, M. (PI)

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 interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.
Terms: Aut, Win | Units: 3-4 | Grading: Letter or Credit/No Credit

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: multivariable calculus and linear algebra. Recommended: knowledge of spreadsheets.
Terms: Aut, Sum | Units: 3-4 | Grading: Letter or Credit/No Credit

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 | Grading: Letter or Credit/No Credit

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 | Grading: Letter or Credit/No Credit

MS&E 245A: Investment Science

Basic concepts of modern quantitative finance and investments. Focus is on the financial theory and empirical evidence that are useful for investment decisions. Topics: basic interest rates; evaluating investments: present value and internal rate of return; fixed-income markets: bonds, yield, duration, portfolio immunization; term structure of interest rates; measuring risk: volatility and value at risk; designing optimal portfolios; risk-return tradeoff: capital asset pricing model and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisite: basic preparation in probability, statistics, and optimization.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit

MS&E 252: Decision Analysis I: Foundations of Decision Analysis

Coherent approach to decision making, using the metaphor of developing a structured conversation having desirable properties, and producing actional thought that leads to clarity of action. Socratic instruction; computational problem sessions. Emphasis is on creation of distinctions, representation of uncertainty by probability, development of alternatives, specification of preference, and the role of these elements in creating a normative approach to decisions. Information gathering opportunities in terms of a value measure. Relevance and decision diagrams to represent inference and decision. Principles are applied to decisions in business, technology, law, and medicine. See 352 for continuation.
Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit

MS&E 260: Introduction to Operations Management

Operations management focuses on the effective planning, scheduling, and control of manufacturing and service entities. This course introduces students to a broad range of key issues in operations management. Topics include determination of optimal facility location, production planning, optimal timing and sizing of capacity expansion, and inventory control. Prerequisites: basic knowledge of Excel spreadsheets, probability.
Terms: Aut, Sum | Units: 3 | Grading: Letter or Credit/No Credit
Filter Results:
term offered
updating results...
number of units
updating results...
time offered
updating results...
days
updating results...
UG Requirements (GERs)
updating results...
component
updating results...
career
updating results...
© Stanford University | Terms of Use | Copyright Complaints