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41 - 50 of 205 results for: MS

MED 215C: Health Policy Graduate Student Tutorial III (HRP 201C)

Third in a three-quarter seminar series, the core tutorial is for first-year Health Policy PhD students and all MS Health Policy students. Major themes in fields of study including health insurance, healthcare financing and delivery, health systems and reform and disparities in the US and globally, health and economic development, health law and policy, resource allocation, efficiency and equity, healthcare quality, measurement and the efficacy and effectiveness of interventions. Blocks of session led by Stanford expert faculty in particular fields of study.
Terms: Spr | Units: 1-2

MS&E 20: Discrete Probability Concepts And Models

Fundamental concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, belief networks, random variables, conditioning, and expectation. The course is fast-paced, but it has no prerequisites.
Last offered: Summer 2019 | UG Reqs: WAY-FR

MS&E 33N: How We Decide: Social Choice in the Age of Algorithms (POLISCI 33N)

The digital revolution arrived with the promise of improving human life, including through its ability to transform the way in which we make social decisions. But one of the most common critiques today is that unstructured interactions in social media and online platforms have actually set us back by spreading fake news, amplifying polarization, and failing to aggregate our diverse views and opinions into collective choices that move our society forward. nnHow should social decisions be made in the age of algorithms? We will approach this question through the lens of social choice theory, and connect this theory from economics and political science to the potential design of algorithms that aggregate our diverse preferences and information. We will review various systems of preference and information aggregation in small groups as well as large societies, including voting systems, bargaining protocols, and methods of deliberation. We will also describe decision making problems that ari more »
The digital revolution arrived with the promise of improving human life, including through its ability to transform the way in which we make social decisions. But one of the most common critiques today is that unstructured interactions in social media and online platforms have actually set us back by spreading fake news, amplifying polarization, and failing to aggregate our diverse views and opinions into collective choices that move our society forward. nnHow should social decisions be made in the age of algorithms? We will approach this question through the lens of social choice theory, and connect this theory from economics and political science to the potential design of algorithms that aggregate our diverse preferences and information. We will review various systems of preference and information aggregation in small groups as well as large societies, including voting systems, bargaining protocols, and methods of deliberation. We will also describe decision making problems that arise in modern applications, such as distributed systems like blockchains and Wikipedia, as well as applications of topical interest such as the assignment of children to schools, the design of congressional districts, and the direct involvement of communities in participatory budgeting. nnA key objective of the class will be to get students to think about how social choice theory can be applied to real-life problems through the design of algorithms. There are no prerequisites, but students should come prepared to use high school level mathematics and deductive reasoning.
Terms: Aut | Units: 4

MS&E 52: Introduction to Decision Making

How to ensure focus, discipline, and passion when making important decisions. Comprehensive examples illustrate Decision Analysis fundamentals. Consulting case studies highlight practical solutions for real decisions. Topics: declaring when and how to make a decision, framing and structuring the decision basis, defining values and preferences, creating alternative strategies, assessing unbiased probabilistic judgments, developing appropriate risk/reward and portfolio models, evaluating doable strategies across the range of uncertain future scenarios, analyzing relevant sensitivities, determining the value of additional information, and addressing the qualitative aspects of communication and commitment to implementation. Required for all students are three problem sets, three in-class exams, and a take-home final exam. Students taking the course for 4 units of credit must also complete and present a team project that analyzes a decision currently being made by an organization of their choice. Not intended for MS&E majors.
Last offered: Summer 2020

MS&E 73SI: ASES Entrepreneurship Bootcamp

Practicum designed to introduce freshmen and sophomores to design thinking and entrepreneurship. Students learn how to conduct user interviews, identifynmarket opportunities, find product-market fit, and develop pitch decks. Concludes with a fast-paced problem-solving session and a 'demo day'-style pitch event, in which students pitch their projects to Silicon Valley venture capitalists (VCs), entrepreneurs, and investors. Students interact with highly experienced speakers, gain mentorship from upperclassmen and VCs, and develop a worldwide entrepreneurship network, both in and out of Silicon Valley. No background knowledge or experience is necessary, only willingness to work hard, get your hands dirty, and learn. Limited enrollment. Application required: https://tinyurl.com/bootcamp2021.
Terms: Aut | Units: 1
Instructors: Eesley, C. (PI)

MS&E 79SI: Values and Principles in the Workplace: PEAK Fellows

Extension of the PEAK Fellows program. Serves as an opportunity for students to explore what it means to create and work for principled, entrepreneurial businesses. Through readings and peer-led discussions, students will definentheir personal set of values and principles to serve as a guide in shaping future teams and workplaces. Prerequisite: admission to PEAK Fellows Program. See https://stvp.stanford.edu/peak-fellows.
Terms: Aut | Units: 1
Instructors: Byers, T. (PI)

MS&E 92: Introduction to Health Policy Modeling

The application of mathematical models to problems in health policy. Estimating the benefits, harms, costs, and uncertainties of a health policy or intervention. Understanding concepts of cost-effectiveness analysis. Developing decision models that capture the tradeoffs between policy alternatives. Examples include disease screening, prevention, and treatment, combating the opioid epidemic, and protecting the blood supply. As a course project, students will develop a simple decision model to evaluate a current health policy problem.
Last offered: Summer 2019

MS&E 92Q: International Environmental Policy

Preference to sophomores. Science, economics, and politics of international environmental policy. Current negotiations on global climate change, including actors and potential solutions. Sources include briefing materials used in international negotiations and the U.S. Congress.
Terms: Win | Units: 3
Instructors: Weyant, J. (PI)

MS&E 108: Senior Project

Restricted to MS&E majors in their senior year. Students carry out a major project in groups of four, applying techniques and concepts learned in the major. Project work includes problem identification and definition, data collection and synthesis, modeling, development of feasible solutions, and presentation of results. Service Learning Course (certified by Haas Center). Satisfies the WIM requirement for MS&E majors.
Terms: Win | Units: 5

MS&E 111: Introduction to Optimization (ENGR 62, MS&E 211)

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: Spr, Sum | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR
Instructors: Goel, A. (PI)
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