printer friendly pageCME 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)
CME 251: Geometric and Topological Data Analysis (CS 233)
Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces. Linear and non-linear dimensionality reduction techniques. Graph representations of data and spectral methods. The rudiments of computational topology and persistent homology on sampled spaces, with applications. Global and local geometry descriptors allowing for various kinds of invariances. Alignment, matching, and map/correspondence computation between geometric data sets. Annotation tools for geometric data. Geometric deep learning on graphs and sets. Function spaces and functional maps. Networks of data sets and joint learning for segmentation and labeling. Prerequisites: discrete algorithms at the level of
CS161; linear algebra at the level of Math51 or
CME103.
Terms: Win, Spr
| Units: 3
Instructors:
Guibas, L. (PI)
;
Weng, Y. (TA)
CME 262: Imaging with Incomplete Information (CEE 260G, GEOPHYS 260G)
Statistical and computational methods for inferring images from incomplete data. Bayesian inference methods are used to combine data and quantify uncertainty in the estimate. Fast linear algebra tools are used to solve problems with many pixels and many observations. Applications from several fields but mainly in earth sciences. Prerequisites: Linear algebra and probability theory.
Terms: Spr
| Units: 3-4
Instructors:
Kitanidis, P. (PI)
CME 263: Introduction to Linear Dynamical Systems (EE 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular-value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer-matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: Linear algebra and matrices as in
ENGR 108 or
MATH 104; ordinary differential equations and Laplace transforms as in
EE 102B or
CME 102.
Terms: Aut
| Units: 3
CME 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CS 279)
Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background (
CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut
| Units: 3
Instructors:
Dror, R. (PI)
;
Bresette, L. (TA)
;
Li, D. (TA)
;
McAvity, J. (TA)
;
Sayana, R. (TA)
;
Suriana, P. (TA)
;
Xu, J. (TA)
CME 291: Master's Research
Students require faculty sponsor. (Staff)
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
for credit
Instructors:
Adeli, E. (PI)
;
Ashlagi, I. (PI)
;
Begenau, J. (PI)
...
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Instructors:
Adeli, E. (PI)
;
Ashlagi, I. (PI)
;
Begenau, J. (PI)
;
Biondi, B. (PI)
;
Bohg, J. (PI)
;
Bustamante, C. (PI)
;
Darve, E. (PI)
;
Dunham, E. (PI)
;
Ermon, S. (PI)
;
Fatahalian, K. (PI)
;
Fedkiw, R. (PI)
;
Gerritsen, M. (PI)
;
Gevaert, O. (PI)
;
Giesecke, K. (PI)
;
Glynn, P. (PI)
;
Goel, A. (PI)
;
Gous, A. (PI)
;
Grundfest, J. (PI)
;
Hanson, K. (PI)
;
Hernandez-Boussard, T. (PI)
;
Iaccarino, G. (PI)
;
Leskovec, J. (PI)
;
Li, F. (PI)
;
Marsden, A. (PI)
;
Mignot, E. (PI)
;
Mirhoseini, A. (PI)
;
Narayan, S. (PI)
;
Onori, S. (PI)
;
Osgood, B. (PI)
;
Palacios, J. (PI)
;
Papanicolaou, G. (PI)
;
Pavone, M. (PI)
;
Pelger, M. (PI)
;
Rao, A. (PI)
;
Re, C. (PI)
;
Rivas, M. (PI)
;
Ross, E. (PI)
;
Rusu, M. (PI)
;
Salzman, J. (PI)
;
Shetty, A. (PI)
;
Suckale, J. (PI)
;
Tchelepi, H. (PI)
;
Tran, K. (PI)
;
Udell, M. (PI)
;
Ugander, J. (PI)
;
Wetzstein, G. (PI)
;
Wootters, M. (PI)
;
Xing, L. (PI)
;
Ying, L. (PI)
;
Wang, J. (TA)
CME 294: Computational Symbolic Mathematics (SYMSYS 294)
Computational symbolic mathematics is a one-unit hands-on seminar course on the use of sophisticated computer algebra systems for addressing mathematical problems that are primarily or entirely symbolic (rather than numerical). Examples will come from the undergraduate curriculum including calculus, differential equations, linear algebra, probability and statistics, and symbolic logic. Students will program in Mathematica but the principles presented apply as well to Mathics, SymPy, Matlab's Symbolic math toolbox, and other systems. (No prior experience with symbol-manipulation programs is assumed.) Students will learn how to exploit special functionality of symbol-manipulating programs, such as Gr¿bner bases. Class lectures will be highly interactive, where both the instructor and students program. There will be weekly homework assignments and a final programming exam.
Terms: Spr
| Units: 1
Instructors:
Stork, D. (PI)
CME 298: Probability and Stochastic Differential Equations for Applications (MATH 158)
Calculus of random variables and their distributions with applications. Review of limit theorems of probability and their application to statistical estimation and basic Monte Carlo methods. Introduction to Markov chains, random walks, Brownian motion and basic stochastic differential equations with some applications in science and/or engineering. Prerequisites:
Math 53 and introductory probability (such as
Stats 116 or
Math 151).
Terms: Spr
| Units: 4
Instructors:
Adhikari, A. (PI)
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