printer friendly pageCME 214: Software Design in Modern Fortran for Scientists and Engineers
This course introduces software design and development in modern Fortran. Course covers the functional, object-oriented-, and parallel programming features introduced in the Fortran 95, 2003, and 2008 standards, respectively, in the context of numerical approximations to ordinary and partial differential equations; introduces object-oriented design and design schematics based on the Unified Modeling Language (UML) structure, behavior, and interaction diagrams; cover the basic use of several open-source tools for software building, testing, documentation generation, and revision control. Recommended: Familiarity with programming in Fortran 90, basic numerical analysis and linear algebra, or instructor approval
Last offered: Autumn 2017
CME 215: Machine Learning and the Physical Sciences (GEOPHYS 148, GEOPHYS 248)
This course provides a survey of the rapidly growing field of machine learning in the physical sciences. It covers various areas such as inverse problems, emulating physical processes, model discovery given data, and solution discovery given equations. It both introduces the background knowledge required to implement physics-informed deep learning and provides practical in-class coding exercises. Students have the opportunity to apply this emerging methodology to their own research interests across all fields of the physical sciences, including geophysics, climate, fluids, or other systems where the same technique applies. Students develop individual projects throughout the semester. Recommended Prerequisite: Calculus (e.g.
Math 21), Differential Equations (e.g.
MATH 53 or
PHYSICS 111) or equivalents.
Terms: Spr
| Units: 3
Instructors:
Lai, C. (PI)
CME 216: Machine Learning for Computational Engineering. (ME 343)
Linear and kernel support vector machines, deep learning, deep neural networks, generative adversarial networks, physics-based machine learning, forward and reverse mode automatic differentiation, optimization algorithms for machine learning, TensorFlow, PyTorch.
Terms: Win
| Units: 3
Instructors:
Patel, D. (PI)
;
Woringer, P. (TA)
CME 217: Analytics Accelerator (BIODS 217)
This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics. Students apply a computational and data analytics lens and use design thinking methodology. The course exposes students to ethics, unintended consequences and team building exercises supported by relevant lectures on data science and subject matter topics. Pre-requisites: none. Enrollment by application only. Graduate students only. The course application closes November 30, 2021. Application and more information:
https://forms.gle/VW6KKWN4AUV6cPzZA
Last offered: Winter 2022
| Repeatable
2 times
(up to 6 units total)
CME 217A: Analytics Accelerator Seminar (BIODS 217A)
CME 217A introduces students to potential computational mathematics research projects at Stanford and with outside organizations. This seminar series is an introduction to winter quarter
CME 217B, a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research. Each week throughout the quarter, a project mentor will present their research. In November, students preference projects and apply for the winter quarter
CME 217B. Pre-requisites: none. Graduate students only.
Last offered: Autumn 2021
| Repeatable
2 times
(up to 2 units total)
CME 218: Applied Data Science (MS&E 218)
This is a multidisciplinary graduate level course designed to give students hands-on experience working in teams through real-world project-based research and experiential classroom activities. Students work in dynamic teams with the support of course faculty and mentors, researching preselected topics. Students apply a computational and data analytics lens and use design thinking methodology. The course exposes students to important techniques in applied data science as well as to the soft skills necessary for success in applied data science, such as ethics, unintended consequences and team building. Enrollment by application only. Graduate students only. The course application closes Sept 25, 2023. Application and more information:
https://forms.gle/gzGXkJmGMVYuJabK7
Terms: Aut
| Units: 3
| Repeatable
2 times
(up to 6 units total)
Instructors:
Hanson, K. (PI)
;
Udell, M. (PI)
CME 229: Applications of machine learning to electronic markets
In this 10-week course, students will learn to apply the techniques of modern machine learning (such as neural networks, reinforcement learning, generative adversarial networks, etc.) to electronic markets. Topics covered will include the fundamentals of financial electronic markets, market simulation, reinforcement learning for market-making and algorithmic execution, as well as predictive and generative modeling for financial markets. Assignments for this course will consist of a mixture of theoretical and coding exercises, and will expose students to real-life financial markets datasets. Throughout the course, students will be introduced to the latest academic and industry research papers, and will complete the course by working on a project of their choice. Course prerequisites: familiarity with optimization and statistics, and ability to code in Python. Open to Graduate Students and Senior-status Undergraduates. Others may request instructor permission to enroll.
Terms: Win
| Units: 3
Instructors:
Vyetrenko, S. (PI)
CME 241: Foundations of Reinforcement Learning with Applications in Finance (MS&E 346)
This course is taught in 3 modules - (1) Markov Processes and Planning Algorithms, including Approximate Dynamic Programming (3 weeks), (2) Financial Trading problems cast as Stochastic Control, from the fields of Portfolio Management, Derivatives Pricing/Hedging, Order-Book Trading (2 weeks), and (3) Reinforcement Learning Algorithms, including Monte-Carlo, Temporal-Difference, Batch RL, Policy Gradient (4 weeks). The final week will cover practical aspects of RL in the industry, including an industry guest speaker. The course emphasizes the theory of RL, modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of programming exercises. No pre-requisite coursework expected, but a foundation in undergraduate Probability, basic familiarity with Finance, and Python programming skills are required.
Terms: Win
| Units: 3
Instructors:
Rao, A. (PI)
;
Zanotti, G. (TA)
CME 243: Risk Analytics and Management in Finance and Insurance (STATS 243)
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.
Terms: Spr
| Units: 3
CME 250: Introduction to Machine Learning
A Short course presenting the principles behind when, why, and how to apply modern machine learning algorithms. We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-fitting/under-fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data. The principles behind various algorithms--the why and how of using them--will be discussed, while some mathematical detail underlying the algorithms--including proofs--will not be discussed. Unsupervised machine learning algorithms presented will include k-means clustering, principal component analysis (PCA), and independent component analysis (ICA). Supervised machine learning algorithms presented will include support vector machines (SVM), classification and regression trees (CART), boosting, bagging, and random forests. Imputation, the lasso, and cross-validation concepts will also be covered. The R programming language will be used for examples, though students need not have prior exposure to R. Prerequisite: undergraduate-level linear algebra and statistics; basic programming experience (R/Matlab/Python).
Terms: Spr
| Units: 1
Instructors:
Sun, C. (PI)
Filter Results: