Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• GRS MA 751: Statistical Machine Learning
  Foundations and applications of statistical machine learning. Supervised and unsupervised learning. Machine classification and regression methods, regularized basis methods, kernel methods, boosting, neural networks, support vector machines, and graphical models.
• GRS MA 765: Time Series Analysis for Neuroscience Research
  Provides an overview of statistical time-series modeling for neuroscience applications. Topics include regression and generalized linear modeling, state space modeling, and parametric and nonparametric spectral analysis. Special emphasis on reading and discussing applications in recent literature.
• GRS MA 770: Mathematical and Statistical Methods of Bioinformatics
  Mathematical and statistical bases of bioinformatics methods and their applications. Hidden Markov models, kernel methods, mathematics of machine learning approaches, probabilistic sequence alignment, Markov chain Monte Carlo and Gibbs sampling, mathematics of phylogenetic trees, and statistical methods in microarray analysis.
• GRS MA 771: Introduction to Dynamical Systems
  Diffeomorphisms and flows; periodic points, nonwandering points, and recurrent points; hyperbolicity, topological conjugacy, and structural stability; stable manifold theorem; symbolic dynamics; Axiom A and chaotic systems.
• GRS MA 775: Ordinary Differential Equations
  Stable and center manifolds theorem, linearization of vector fields, variational equations, Floquet theory and Poincare; maps for periodic orbits, bifurcation of rest points, averaging theory, topics from singular perturbations, Hamiltonian systems, non-linear oscillations, normal forms, and applications.
• GRS MA 776: Partial Differential Equations
  Hyperbolic, elliptic, and parabolic equations. Characteristics and separation of variables. Eigenvalue problems, Fourier techniques, Sobolev spaces, and potential theory. Introduction to pseudodifferential operators.
• GRS MA 777: Multiscale Methods for Stochastic Processes and Differential Equations
  Methods and models for the analysis of systems that possess many characteristic length and time scales. Asymptotic expansions, coarse-graining of multiscale stochastic models, mathematical analysis and statistical inference. Balance of theory and concepts illustrated via various applications.
• GRS MA 779: Probability Theory I
  Introduction to probability with measure theoretic foundations. Fundamentals of measure theory. Probability space. Measurable functions and random variables. Expectation and conditional expectation. Zero-one laws and Borel-Cantelli lemmas. Chracteristic functions. Modes of convergence. Uniform integrability. Skorokhod representation theorem. Basic limit theorems.
• GRS MA 780: Probability Theory II
  Probability topics important in applications and research. Laws of large numbers. Three series theorem. Central limit theorems for independent and non-identically distributed random variables. Speed of convergence. Large deviations. Laws of the iterated logarithm. Stable and infinitely divisible distributions. Discrete time martingales and applications.
• GRS MA 781: Estimation Theory
  Review of probability, populations, samples, sampling distributions, and delta theorems. Parametric point estimation. Rao-Cramer inequality, sufficient statistics, Rao-Blackwell theorem, maximum likelihood estimation, least squares estimation, and general linear model of full rank. Confidence intervals. Bayesian analysis and decision theory.
• GRS MA 782: Hypothesis Testing
  Parametric hypothesis testing, uniformly and locally the most powerful tests, similar tests, invariant tests, likelihood ratio tests, linear model testing, asymptotic theory of likelihood ratio, and chi-squared test. Logit and log-lin analysis of contingency tables.
• GRS MA 783: Advanced Stochastic Processes
  Proof-based approach to stochastic processes. Brownian motion. Continuous martingales. Stochastic integration. Ito formula. Girsanov's Theorem. Stochastic differential equations. Feynman-Kac formula. Markov Processes. Local times. Levy processes. Semimartingales and the general stochastic integral. Stable processes. Fractional Brownian motion.
• GRS MA 822: Topics in Geometry and Topology
  Advanced seminar in topics in differential geometry, topology and mathematical physics of current research interest.
• GRS MA 841: Seminar: Algebra
  
• GRS MA 842: Seminar: Algebra
  
• GRS MA 861: Seminar: Applied Mathematics
  
• GRS MA 876: Seminar: Partial Differential Equations
  
• GRS MA 881: Seminar: Statistics
  Advanced seminar in topics in statistics of current research interest.
• GRS MA 882: Seminar: Statistics
  Advanced seminar in topics in statistics of current research interest.
• GRS MA 901: Internship in Statistics
  For masters students in statistics, entailing substantive experience in practical application of statistics in domain areas like biomedicine, finance, marketing, and similar. May be taken only once, with approval of the Director of the MSSP Program. Bi-weekly and final reports required.

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