71 - 80 of 236 results for: MS

With the recent developments in generative AI, the value in human creativity is increasingly a focus. Course draws from lessons from creativity in the arts to teach engineering students methods for creativity derived from musicians and artists. For our engineering students to learn creativity as a skill that is distinguishable and differentiated from generative artificial intelligence, this course explores, for instance the anatomy of a Hollywood pop song and the process behind the creation of globally impactful art. Students learn how to transfer these skills into the creation of engaging entrepreneurial solutions, for effective storytelling, and in developing their unique personal and professional stories. Students learn skills to unlock creative power which they will apply in the course as a design vehicle for a wide range of applications in engineering, self-expression, technological exploration, and the development of solutions that are centered around human connection and emotional engagement with the user. Sessions are practical, drawing tools and lessons from interdisciplinary individuals with wide-ranging careers. No artistic or entrepreneurial experience necessary.

Extension of the Xfund Ethics 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 define their personal set of values and principles to serve as a guide in shaping future teams and workplaces. Prerequisite: admission to Xfund Ethics Fellows Program. See https://stvp.stanford.edu/xef.

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.

MS&E seniors 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 mandatory in-person presentation of results on last day of classes. Satisfies the WIM requirement for MS&E majors. This is a Cardinal Course certified by the Haas Center for Public Service.

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.

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.

Introduction to optimization theory, modeling, structure, and methods with focus on the mathematical foundations. Accelerated introduction to linear programming, nonlinear optimization, and optimization algorithm design. Prerequisite: CME 100 or MATH 51 or equivalent.

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, and programming methodology.

Probability is the foundation behind many important disciplines including statistics, machine learning, risk analysis, stochastic modeling and optimization. This course provides an in-depth undergraduate-level introduction to fundamental ideas and tools of probability. Topics include: the foundations (sample spaces, random variables, probability distributions, conditioning, independence, expectation, variance), a systematic study of the most important univariate and multivariate distributions (Normal, Multivariate Normal, Binomial, Poisson, etc...), as well as a peek at some limit theorems (basic law of large numbers and central limit theorem) and, time permitting, some elementary markov chain theory. Prerequisite: CME 100 or MATH 51.