31 - 40 of 94 results for: CME

Provides an introductory overview of modern computational methods for problems arising primarily in mechanics of solids and is intended for students from various engineering disciplines. The course reviews the basic theory of linear solid mechanics and introduces students to the important concept of variational forms, including the principle of minimum potential energy and the principles of virtual work. Specific model problems that will be considered include deformation of bars, beams and membranes, plates, and problems in plane elasticity (plane stress, plane strain, axisymmetric elasticity). The variational forms of these problems are used as the starting point for developing the finite element method (FEM) and boundary element method (BEM) approaches ­ providing an important connection between mechanics and computational methods.

The course explores the underlying network structure of our social, economic, and technological worlds and uses techniques from graph theory and economics to examine the structure & evolution of information networks, social contagion, the spread of social power and popularity, and information cascades. Prerequisites: basic graph and probability theory.

Survey the current Financial Technology landscape through the lens of Artificial Intelligence applications, with emphasis in 4 areas: Payments, Blockchain and Cryptocurrencies, Robo-Advisory, and Marketplace Lending. Students work in groups of 4 to develop an original financial technology project, research paper or product prototype within a chosen area. Final project posters to be presented to the class and posted online. Top posters to be selected and presented at the Stanford Financial Technology conference in January. Classes will alternate between industry speakers, lectures and scheduled group meetings with teaching team. Advanced undergraduates, graduate students, and students from other Schools are welcome to enroll. Prerequisites: Basic programming skills, knowledge of design process, introductory statistics. No formal finance experience required. Enrollment is capped at 32.

Topics of current interest. May be repeated for credit.

Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite: STATS 240 or equivalent.

For graduate students in the MCF track; students will work individually or in groups on research projects.

Description: Current topics for enrolled students in the MCF program: This course is an introduction to computational, statistical, and optimizations methods and their application to financial markets. Class will consist of lectures and real-time problem solving. Topics: Python & R programming, interest rates, Black-Scholes model, financial time series, capital asset pricing model (CAPM), options, optimization methods, and machine learning algorithms. Appropriate for anyone with a technical and solid applied math background interested in honing skills in quantitative finance. Prerequisite: basic statistics and exposure to programming.Can be repeated up to three times.

Teams of students use techniques in applied and computational mathematics to tackle problems with real world data sets. Application of design methodology adapted for data analysis will be emphasized; leverage design thinking to come up with efficient and effective data driven insights; explore design thinking methodology in small group setting.;apply design thinking to a specific data centric problem and make professional group presentation of the results. Limited enrollment. Prerequisites: CME100/102/104 or equivalents, or instructor consent. Recommended: CME106/108 and familiarity with programming at the level of CME 192/193.