printer friendly pageMS&E 206: Incentives in Computer Science (CS 269I)
Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems. Topics include auction and contest design, equilibrium analysis, cryptocurrencies, design of networks and network protocols, reputation systems, social choice, and social network analysis. Case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft. Prerequisites:
CS106B/X and
CS161, or permission from the instructor.
Terms: Spr
| Units: 3
Instructors:
Rubinstein, A. (PI)
MS&E 208A: 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:
https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors:
Brandeau, M. (PI)
;
Katila, R. (PI)
MS&E 208B: 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:
https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors:
Brandeau, M. (PI)
;
Katila, R. (PI)
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:
https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors:
Brandeau, M. (PI)
;
Katila, R. (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:
https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors:
Brandeau, M. (PI)
;
Katila, R. (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:
https://forms.gle/bFtMtwJMyaCJRhkf8 with statement and offer letter.
Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
15 times
(up to 15 units total)
Instructors:
Brandeau, M. (PI)
;
Katila, R. (PI)
MS&E 211: Introduction to Optimization (ENGR 62, MS&E 111)
Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite:
CME 100 or
MATH 51.
Terms: Aut
| Units: 3-4
Instructors:
Ye, Y. (PI)
;
Herrscher, J. (TA)
;
Hu, C. (TA)
;
Sun, C. (TA)
;
Zhang, E. (TA)
;
Zhang, X. (TA)
MS&E 211DS: Introduction to Optimization: Data Science (MS&E 111DS)
Formulation and computational analysis of linear, discrete, and other optimization problems. Strong emphasis on data science and machine learning applications, as well as applications in matching and pricing in online markets. Prerequisite:
CME 100 or
MATH 51.
Terms: Win
| Units: 3-4
MS&E 211X: Introduction to Optimization (Accelerated) (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: Spr
| Units: 3-4
MS&E 212: Graph and Combinatorial Optimization (MS&E 112)
Optimization problems dealing with graph structure. Topics: introduction to graph theory; combinatorial optimization problems on networks including network flows, matching, and assignment problems; NP-completeness and approximation algorithms; applications in the study of social networks, market design, and bioinformatics. Prerequisites: basic concepts in linear algebra, probability theory,
CS 106A or X.
Terms: Win
| Units: 3
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