printer friendly pageMS&E 237A: Bandit Learning: Behaviors and Applications (EE 277)
The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g.,
CS106B), probability (e.g., MS&E 121,
EE 178 or
CS 109), machine learning (e.g.,
EE 104/
CME 107, MS&E 226 or
CS 229).
Terms: Aut
| Units: 3
Instructors:
Van Roy, B. (PI)
;
Kagrecha, A. (TA)
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
Instructors:
Pelger, M. (PI)
;
Archetti, E. (TA)
;
Duan, J. (TA)
...
more instructors for MS&E 245A »
Instructors:
Pelger, M. (PI)
;
Archetti, E. (TA)
;
Duan, J. (TA)
;
Fan, Y. (TA)
;
Lerner, S. (TA)
;
Zanotti, G. (TA)
MS&E 271: Global Entrepreneurial Marketing
Introduces core marketing concepts to bring a new product or service to market and build for its success. Geared to both entrepreneurs and intrapreneurs alike who have a passion for innovation. Course themes include: Identifying markets and opportunities, defining the offering and customer experience, creating demand, generating revenue, and measuring success. The team-based final focuses on developing a go-to-market strategy based on concepts from the course. Learn about managing self, building culture and teams, strategically think about your contribution as entrepreneur or intrapreuneur to an organization, community or society at large. Highly experiential and project based. Limited enrollment.
Terms: Aut
| Units: 3-4
MS&E 278: Patent Law and Strategy for Innovators and Entrepreneurs (ENGR 208)
This course teaches the essentials for a startup founder to build a valuable patent portfolio and avoid a patent infringement lawsuit. Jeffrey Schox and Diana Lin are partners at Schox Patent Group, which is the law firm that wrote the patents for Coinbase, Cruise, Duo, Joby, Twilio and 500+ other startups that have collectively raised over $10B in venture capital. This course, which was previously called
ME 208, is appropriate for students with any engineering background. For those students who are interested in a career in Patent Law, please note that this course is a prerequisite for ME238 Patent Prosecution. There are no prerequisites for this course, but the student must be at the senior or graduate level.
Terms: Aut
| Units: 2-3
MS&E 280: Organizational Behavior: Evidence in Action
Organization theory; concepts and functions of management; behavior of the individual, work group, and organization. Emphasis is on cases and related discussion. Limited enrollment.
Terms: Aut
| Units: 3-4
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
Instructors:
Blank, S. (PI)
;
Brown, M. (PI)
;
Felter, J. (PI)
;
Jonga, S. (TA)
;
Lakhtakia, S. (TA)
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
Instructors:
Ashlagi, I. (PI)
;
Bambos, N. (PI)
;
Blanchet, J. (PI)
...
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Instructors:
Ashlagi, I. (PI)
;
Bambos, N. (PI)
;
Blanchet, J. (PI)
;
Brandeau, M. (PI)
;
Eesley, C. (PI)
;
Eisenhardt, K. (PI)
;
Giesecke, K. (PI)
;
Glynn, P. (PI)
;
Goel, A. (PI)
;
Hinds, P. (PI)
;
Johari, R. (PI)
;
Karunakaran, A. (PI)
;
Katila, R. (PI)
;
Lo, I. (PI)
;
Pate-Cornell, E. (PI)
;
Pelger, M. (PI)
;
Saberi, A. (PI)
;
Shachter, R. (PI)
;
Sheares, A. (PI)
;
Sidford, A. (PI)
;
Sutton, R. (PI)
;
Sweeney, J. (PI)
;
Syrgkanis, V. (PI)
;
Tse, E. (PI)
;
Udell, M. (PI)
;
Ugander, J. (PI)
;
Valentine, M. (PI)
;
Van Roy, B. (PI)
;
Vitercik, E. (PI)
;
Weyant, J. (PI)
;
Ye, Y. (PI)
MS&E 302: Fundamental Concepts in Management Science and Engineering
Each course session will be devoted to a specific MS&E PhD research area. Advanced students will make presentations designed for first-year doctoral students regardless of area. The presentations will be devoted to: illuminating how people in the area being explored that day think about and approach problems, and illustrating what can and cannot be done when addressing problems by deploying the knowledge, perspectives, and skills acquired by those who specialize in the area in question. Area faculty will attend and participate. During the last two weeks of the quarter groups of first year students will make presentations on how they would approach a problem drawing on two or more of the perspectives to which they have been exposed earlier in the class. Attendance is mandatory and performance will be assessed on the basis of the quality of the students¿ presentations and class participation. Restricted to first year MS&E PhD students.
Terms: Aut
| Units: 1
Instructors:
Bambos, N. (PI)
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)
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