The This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. Prior to the start of the work experience, students secure faculty consent for an independent study project to be completed during the internship quarter. Explore fields of academic study by taking for-credit classes at the University of Chicago College, in the Graduate Divisions of the Biological Sciences, Physical Sciences, Social Sciences, and Humanities, and at the graduate schools in Divinity, Public Policy, Social Work, Business, and Law as a graduate … The Department of Statistics invites faculty, staff, and students of the University to participate in its consulting program. The second half of the class will focus on individualized data analysis projects. Fundamentals of Computational Biology: Algorithms and Applications. Longitudinal data consist of multiple measures over time on a sample of individuals. Wu     Terms Offered: TBD. While geometric perspectives will be emphasized, assignments will also introduce asymptotic methods for analysis and use numerical simulation as an exploratory tool. STAT 31300. Topics include standard distributions (i.e. Terms Offered: Spring This seminar course is an internal training program for graduate students in Statistics. Applied Stochastic Processes. Differential Equation 100 Units. Terms Offered: To be determined 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). Instructor(s): D. Hedeker     Terms Offered: Spring STAT 31010. 100 Units. The course also discusses volatility clustering and market microstructure. 100 Units. Epidemiologic Methods. Note(s): Students with credit for MATH 235 should not enroll in STAT 312. STAT 37790. Terms Offered: To be determined. The course is focused on the statistical theory of how to connect the two, but there will also be some data analysis. Problems associated with multiple time scales will be discussed along with methods to address them (implicit discretizations, multiscale methods and dimensional reduction). Fast Algorithms. Time-series Analysis for Forecasting and Model Building. 3.Sequential State The final project will be to write the "specific aims" and "methods" sections of a research proposal on a topic of the student's choice. Terms Offered: Spring Topics in Statistical Machine Learning" is a second graduate level course in machine learning, assuming students have had previous exposure to machine learning and statistical theory. Prerequisite(s): STAT 25100 and MATH 20500; STAT 30400 or consent of instructor Graduate students in Statistics or Financial Mathematics can enroll without prerequisites. Via ZOOM, George Herbert Jones Laboratory It has also claimed a disproportionate share of the honors the economics profession can bestow. This course will study the problem of selective inference where we would like to provide statistical guarantees about hypotheses or parameters whose definitions are influenced by our analysis of the same data set. Terms Offered: Not offered in 2020-2021. This course will begin with an overview of the theory for Gaussian processes, with a focus on stationary processes and their associated spectral properties and how these relate to problems of spatial interpolation. STAT 32900. Prerequisite(s): STAT 30100 and STAT 30400 and STAT 31015, or consent of instructor. Instructor(s): Dana Mendelson (Math) and Rebecca Willett (CS/Stats)      Terms Offered: Autumn The Department of Statistics offers two tracks of graduate study, one leading to the Master of Science (M.S.) 100 Units. Applied Analysis. Students will gain an exposure to the theoretical basis for these methods as well as their practical application in numerical computations. STAT 30400. Examples which will be discussed are butterfly algorithms, fast multipole methods, fast direct solvers, and hierarchical matrix compression. 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. This course investigates the dynamic relationships between variables. Prerequisite(s): ODEs and/or dynamical systems at an undergraduate level or consent of instructor. Prerequisite(s): Recommended 38100/38300 sequence, or experience with measure-theoretical probability. The field of statistics has become a core component of research in the biological, physical, and social sciences, as well as in traditional computer science domains such as artificial intelligence. 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