MS&E 316: Discrete Mathematics and Algorithms (CME 305)
Topics: Basic Algebraic Graph Theory, Matroids and Minimum Spanning Trees, Submodularity and Maximum Flow, NPHardness, Approximation Algorithms, Randomized Algorithms, The Probabilistic Method, and Spectral Sparsification using Effective Resistances. Topics will be illustrated with applications from Distributed Computing, Machine Learning, and largescale Optimization. Prerequisites:
CS 261 is highly recommended, although not required.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
MS&E 317: Algorithms for Modern Data Models (CS 263)
We traditionally think of algorithms as running on data available in a single location, typically main memory. In many modern applications including web analytics, search and data mining, computational biology, finance, and scientific computing, the data is often too large to reside in a single location, is arriving incrementally over time, is noisy/uncertain, or all of the above. Paradigms such as mapreduce, streaming, sketching, Distributed Hash Tables, Bulk Synchronous Processing, and random walks have proved useful for these applications. This course will provide an introduction to the design and analysis of algorithms for these modern data models. Prerequisite: Algorithms at the level of
CS 261.
Terms: not given this year, last offered Spring 2015

Units: 3

Grading: Letter or Credit/No Credit
MS&E 318: LargeScale Numerical Optimization (CME 338)
The main algorithms and software for constrained optimization emphasizing the sparsematrix methods needed for their implementation. Iterative methods for linear equations and least squares. The simplex method. Basis factorization and updates. Interior methods. The reducedgradient method, augmented Lagrangian methods, and SQP methods. Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparsematrix methods, and gradientbased algorithms for constrained optimization. Recommended: MS&E 310, 311, 312, 314, or 315;
CME 108, 200, 302, 304, 334, or 335.
Terms: Spr

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Saunders, M. (PI)
MS&E 319: Approximation Algorithms
Combinatorial and mathematical programming techniques to derive approximation algorithms for NPhard optimization problems. Prossible topics include: greedy algorithms for vertex/set cover; rounding LP relaxations of integer programs; primaldual algorithms; semidefinite relaxations. May be repeated for credit. Prerequisites: 112 or
CS 161.
Terms: not given this year

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
MS&E 321: Stochastic Systems
Topics in stochastic processes, emphasizing applications. Markov chains in discrete and continuous time; Markov processes in general state space; Lyapunov functions; regenerative process theory; renewal theory; martingales, Brownian motion, and diffusion processes. Application to queueing theory, storage theory, reliability, and finance. Prerequisites: 221 or
STATS 217;
MATH 113, 115. (Glynn)
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Blanchet Mancilla, J. (PI)
MS&E 322: Stochastic Calculus and Control
Ito integral, existence and uniqueness of solutions of stochastic differential equations (SDEs), diffusion approximations, numerical solutions of SDEs, controlled diffusions and the HamiltonJacobiBellman equation, and statistical inference of SDEs. Applications to finance and queueing theory. Prerequisites: 221 or
STATS 217:
MATH 113, 115.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
MS&E 324: Stochastic Methods in Engineering (CME 308, MATH 228)
The basic limit theorems of probability theory and their application to maximum likelihood estimation. Basic Monte Carlo methods and importance sampling. Markov chains and processes, random walks, basic ergodic theory and its application to parameter estimation. Discrete time stochastic control and Bayesian filtering. Diffusion approximations, Brownian motion and an introduction to stochastic differential equations. Examples and problems from various applied areas. Prerequisites: exposure to probability and background in analysis.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Glynn, P. (PI)
MS&E 325: Advanced Topics in Applied Probability
Current stochastic models, motivated by a wide range of applications in engineering, business, and science, as well as the design and analysis of associated computational methods for performance analysis and control of such stochastic systems.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Blanchet Mancilla, J. (PI)
MS&E 326: Advanced Topics in Game Theory with Engineering Applications
Advanced Topics in Game Theory with Engineering Applications
Terms: Spr

Units: 3

Repeatable for credit

Grading: Letter or Credit/No Credit
Instructors:
Johari, R. (PI)
MS&E 330: Law, Order & Algorithms (CSRE 230, SOC 279)
Data and algorithms are transforming law enforcement and criminal justice, a shift that is ripe for rigorous empirical and narrative exploration. This class is centered around several datadriven projects in criminal justice, with the goal of fostering greater understanding, transparency, and public accountability. Students work in interdisciplinary teams, using a combination of statistical and journalistic methods. Some of the work may be published by news organizations or may be used to advance data journalism investigations. Students with a background in statistics, computer science, law, public policy or journalism are encouraged to participate. Enrollment is limited, and project teams will be selected during the first week of class.
Terms: Spr

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Goel, S. (PI)
Filter Results: