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101 - 110 of 162 results for: MS&E

MS&E 296: Technology, Innovation and Great Power Competition (INTLPOL 340)

This course explores how new technologies pose challenges and create opportunities for the United States to compete more effectively with rivals in the international system with a focus on strategic competition with the People's Republic of China. In this experiential policy class, you will address a priority national security challenge employing the "Lean" problem solving methodology to validate the problem and propose a detailed technology informed solution tested against actual experts and stakeholders in the technology and national security ecosystem. The course builds on concepts presented in MS&E 193/293: Technology and National Security and provides a strong foundation for MS&E 297: Hacking for Defense.
Terms: Aut | Units: 4

MS&E 297: "Hacking for Defense": Solving National Security issues with the Lean Launchpad

In a crisis, national security initiatives move at the speed of a startup yet in peacetime they default to decades-long acquisition and procurement cycles. Startups operate with continual speed and urgency 24/7. Over the last few years they've learned how to be not only fast, but extremely efficient with resources and time using lean startup methodologies. In this class student teams will take actual national security problems and learn how to apply lean startup principles, ("business model canvas," "customer development," and "agile engineering) to discover and validate customer needs and to continually build iterative prototypes to test whether they understood the problem and solution. Teams take a hands-on approach requiring close engagement with actual military, Department of Defense and other government agency end-users. Team applications required in February, see hacking4defense.stanford.edu. Limited enrollment.
Terms: Spr | Units: 3-5

MS&E 298: Detecting Discrimination with Data (CSRE 298)

What does it mean for a decision-making process to be discriminatory? How do we quantify inequality? What steps can be taken to mitigate potential bias? This hands-on course explores legal and statistical conceptions of discrimination using examples from public policy, healthcare, economics, technology, and education. Each session will consist of an interactive lecture, a live coding session where we implement techniques from the lecture, and a research paper discussion. The course also features occasional guest speakers from industry and academia. Prerequisites: An introductory statistics course (e.g., 120, 125, 226, or CS 109) and an introductory programming course (e.g., CS 106A). Graduate students may enroll for 1 unit.
Terms: Aut | Units: 1-2
Instructors: Grossman, J. (PI)

MS&E 301: Dissertation Research

Prerequisite: doctoral candidacy.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit

MS&E 310: Linear Programming

Formulation of standard linear programming models. Theory of polyhedral convex sets, linear inequalities, alternative theorems, and duality. Variants of the simplex method and the state of art interior-point algorithms. Sensitivity analyses, economic interpretations, and primal-dual methods. Relaxations of harder optimization problems and recent convex conic linear programs. Applications include game equilibrium facility location. Prerequisite: MATH 113 or consent of instructor.
Terms: Aut | Units: 3
Instructors: Ye, Y. (PI)

MS&E 311: Optimization (CME 307)

Applications, theories, and algorithms for finite-dimensional linear and nonlinear optimization problems with continuous variables. Elements of convex analysis, first- and second-order optimality conditions, sensitivity and duality. Algorithms for unconstrained optimization, and linearly and nonlinearly constrained problems. Modern applications in communication, game theory, auction, and economics. Prerequisites: MATH 113, 115, or equivalent.
Terms: Win | Units: 3
Instructors: Udell, M. (PI)

MS&E 312: Optimization Algorithms (CME 334, CS 369O)

Fundamental theory for solving continuous optimization problems with provable efficiency guarantees. Coverage of both canonical optimization methods and techniques, e.g. gradient descent, mirror descent, stochastic methods, acceleration, higher-order methods, etc. and canonical optimization problems, critical point computation for non-convex functions, smooth-convex function minimization, regression, linear programming, etc. Focus on provable rates for solving broad classes of prevalent problems including both classic problems and those motivated by large-scale computational concerns. Discussion of computational ramifications, fundamental information-theoretic limits, and problem structure. Prerequisite: linear algebra, multivariable calculus, probability, and proofs.
Terms: Win | Units: 3

MS&E 313: Almost Linear Time Graph Algorithms (CS 269G)

Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.
Last offered: Autumn 2018

MS&E 314: Optimization in Data Science and Machine Learning

Optimization in Data Science and Machine Learning
Terms: Win | Units: 3
Instructors: Ye, Y. (PI)
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