Prerequisite(s): STAT 30400 or consent of instructor. Terms Offered: Winter We also cover examples of finite difference schemes; simple stability analysis; convergence analysis and order of accuracy; consistency analysis and errors (i.e., dissipative and dispersive errors); and unconditional stability and implicit schemes. Equivalent Course(s): FINM 34510. The encoding section will cover receptive field analysis STAT 38100. 100 Units. We invite faculty, staff, and students of the University to participate in our consulting program. Prerequisite(s): Consent of instructor. We will study Gaussian approximations and optimization and sampling algorithms, including a variety of Kalman-based and particle filters as well as Markov chain Monte Carlo schemes designed for high-dimensional inverse problems. We will also discuss some applications of these algorithms (as well as commonly used statistical techniques) in genomics and systems biology, including genome assembly, variant calling, transcriptome inference, and so on. We will mainly focus on the discrete perspectives of these models, but will also at times discuss the connections to the continuous counterparts. 100 Units. STAT 31700. Homework assignments are given throughout the quarter. Consultation is provided by graduate students of the Department with guidance from faculty members. There will be a strong emphasis on stochastic processes and inference in complex hierarchical statistical models. topics in sequential parameter and state estimation.The focus of the class is Terms Offered: To be determined Topics for the course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods including matching, stratification, inverse-probability-of-treatment-weighting (IPTW), marginal mean weighting through stratification (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RDD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-section and panel data, and fixed effects model. Equivalent Course(s): STAT 24620. STAT 39000 or STAT 39010 or STAT 38510 are strongly. Generalized Linear Models. NEW! ", Instructor(s): G. Hong Terms Offered: Winter Ph.D. students should also participate in the department's consulting program, which is led by faculty members and exposes the students to empirical projects inside the university. The Department of Statistics offers an exciting and revamped graduate program that prepares students for cutting-edge interdisciplinary research in a wide variety of fields. The estimated graduate school tuition & fees at University of Chicago is $64,241 for academic year 2020-2021. 100 Units. Topics will include discussion of matrix factorizations (including diagonalization, the spectral theorem for normal matrices, the singular value decomposition, and the Schur and polar decompositions), and an overview of classical direct and iterative approaches to numerical methods for problems Students should also have familiarity with the contents of MATH 27300 and MATH 27500 or similar. Applied Multivariate Analysis. Additional topics will be included depending on student interests. This course is designed for graduate students and advanced undergraduate students from the social sciences, education, public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. We will discuss nonparametric Bayesian approaches to mixture models, latent feature models, hierarchical models, network models, and high-dimensional regression models. 100 Units. are introduced including error measures and different notions of numerical Our emphasis is on defining the processes and calculating or approximating various related probabilities. Taking courses with potential advisers is part of this process. Problems associated with multiple time scales will be discussed along with methods to address them (implicit discretizations, multiscale methods and dimensional reduction). 100 Units. Prerequisite(s): Instructor consent. 100 Units. A rich series of interdisciplinary workshops and conferences bring together students and faculty from throughout the university for intellectual exchange. Note(s): undergrads permitted with permission of instructors Students need to be familiar with two out of the following three: probability (no need for measure theory)/statistics/game theory (at intro level). Social Networks, Probability, Learning, and Game Theory. It is also a natural course for more advanced math students who want to broaden their mathematical education and to increase their marketability for nonacademic positions. Introduction to learned emulators: how do they work, where have they been successful so far and what are the goals in this field? Prerequisite(s): STAT 34300, STAT 34700, STAT 34800, and STAT 37601/CMSC 25025, or Terms Offered: To be determined. Prerequisite(s): Consent of instructor. Stochastic Calculus II. We will provide a list of papers covering the above topics and students will be evaluated on in-class presentations. Nonparametric Inference. The course treats nonparametric methodology and its use, together with theory that explains the statistical properties of the methods. Statistical Computing B focuses on common data technology used in statistical computing and broader data science. Prospective Students : (773) 702-3760. Instructor(s): Nathan Srebro Terms Offered: Winter During the first year of the Ph.D. program, students are given a thorough grounding in material that forms the foundations of modern statistics and scientific computation, including data analysis, mathematical statistics, probability theory, applied probability and modeling, and computational methods. Terms Offered: Winter 100 Units. Field Research. Masters Seminar: Statistics. The course ends with an introduction to jump process (Levy processes) and the corresponding integration theory. Prerequisite(s): Instructor consent. 100 Units. We focus on the period from 1650 to 1950, with an emphasis on the mathematical developments in the theory of probability and how they came to be used in the sciences. The first quarter introduces a range of statistical frameworks for finding low-dimensional structure in high-dimensional data, such as sparsity in regression, sparse graphical models, or low-rank structure. This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. STAT 41520. Instructor(s): S. Stigler Terms Offered: Spring Prerequisite(s): Prior exposure to basic calculus and probability theory, CPNS 35500 or instructor consent. Equivalent Course(s): FINM 34500. 100 Units. Topics will include exponential, curved exponential, and location-scale families; mixtures, hierarchical, and conditional modeling including compatibility of conditional distributions; principles of estimation; identifiability, sufficiency, minimal sufficiency, ancillarity, completeness; properties of the likelihood function and likelihood-based inference, both univariate and multivariate, including examples in which the usual regularity conditions do not hold; elements of Bayesian inference and comparison with frequentist methods; and multivariate information inequality. 100 Units. methods in data analysis. Prerequisite(s): ODEs and/or dynamical systems at an undergraduate level or consent of instructor. For linear systems and least squares problems, we will discuss stationary methods (Jacobi, Gauss-Seidel, SOR), semi-iterative methods (Richardson, steepest descent, Chebyshev, conjugate gradient), and Krylov subspace methods (MINRES, SYMMLQ, LSQR, GMRES, QMR, BiCG). 773.702.8333. Equivalent Course(s): FINM 33170. This course is primarily about iterative algorithms in matrix computation. 100 Units. Instructional Professor (open rank) in Data Science. 100 Units. STAT 31521. STAT 31210. STAT 34700. The course is suitable for graduate students and advanced undergraduates in science, engineering, and applied mathematics. 100 Units. 100 Units. STAT 35460. This course will explore modern approaches to optimization, data augmentation, and domain shift for deep neural networks from both theoretical and empirical perspectives. Drawing on these historical and humanistic perspectives, students will have the opportunity to measure and analyze their own lives in terms of data-as well as think critically about the effects of these knowledge practices. STAT 41510. Prerequisite(s): STAT 24500 and STAT 34300, or some background in analysis and previous exposure to stochastic processes. Measure-Theoretic Probability I. Terms Offered: All quarters Graduate students in Statistics or Financial Mathematics can enroll without prerequisites. Course description is subject to change. This class primarily concerns the design and analysis of Monte Carlo sampling techniques for the estimation of averages with respect to high dimensional probability distributions. Topics covered include metric spaces and basic topological notions, aspects of mathematical analysis in several variables, and an introduction to measure and integration. However, little is understood about these emulators. 100 Units. 100 Units. Although an overview Prerequisite(s): Instructor consent. Equivalent Course(s): CAAM 31240. You choose the one that matches your interests, goals, experience, and schedule. 50 Units. Graduate education at the University of Michigan is a shared enterprise. When the course is offered by the Booth school, please visit the Booth portal and search via the course search tool: Brownian Motion and Stochastic Calculus. This course will introduce students to several classes of computational methods broadly referred to as "fast analysis-based algorithms" which exploit information about structure and symmetry to obtain more favorable computational complexity. Terms Offered: Not offered in 2020-2021. This course covers the fundamentals of continuous optimization with an emphasis on algorithmic and computational issues. Fundamentals of Computational Biology: Models and Inference. Equivalent Course(s): STAT 24410. The PDF will include all information unique to this page. The Office of the University Registrar is committed to supporting the university’s academic and administrative operations, as they relate to student success, by providing the data needed to make more informed decisions. Modern research has begun to develop techniques that can be effective in high dimensions, and that can be understood theoretically. 100 Units. Stochastic linear quadratic This didactic course covers the fundamentals of stochastic chemical processes as they arise in the study of gene regulation. Numerical Methods for Stochastic Differential Equations. STAT 37601. With this increased capacity to generate and analyze data, classical statistical methods may no longer ensure the reliability or replicability of scientific discoveries. 100 Units. Current Students : (773) 834-2093 Equivalent Course(s): STAT 27400. Prerequisite(s): Consent of instructor. In addition to the courses, seminars, and programs in the Department of Statistics, courses and workshops of direct interest to statisticians occur throughout the University, most notably in the programs in statistics and econometrics in the Booth School of Business and in the research programs in Health Studies, Human Genetics, Financial Mathematics and Econometrics, Computer Science, Economics and NORC (formerly the National Opinion Research Center). 100 Units. Topics in Machine Learning. probability theory, with applications in a wide range of disciplines (including Data may vary depending on school and academic year. Equivalent Course(s): CAAM 31230. The acceptance ratio at University of Chicago was 6.17% - 34,641 students were applied and 2,137 were admitted to the school. STAT 30900. The stochastic Taylor expansion provides the basis for the discrete-time numerical methods for differential equations. This course is an introduction to scientific computing using the Python programming language intended to prepare students for further computational work in courses, research, and industry. 100 Units. The class will begin with an introduction to the numerical simulation of continuous time Markov processes including the discretization of stochastic (and ordinary) differential equations. The course is focused on the statistical theory of how to connect the two, but there will also be some data analysis. 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