27 courses with the subject MATG, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MATG 511Computational Methods for Analytics3
This course is a survey of programming tools used in solving problems in applied mathematics and data analytics. The course material comprises the following broad areas: programming techniques in scientific languages such as MATLAB, Python, and R; and an overview of selected topics in data handling, data visualization, and introductory predictive analytics. Additional topics may include analytics-related topics in linear algebra and numerical analysis. A final project is required for this course. Prerequisites: CMPT 101 and MATH 285 or MATH 287 and MATH 372 or MATH 351.
Topics covered in this course include sampling distributions, point estimation, interval estimation, testing statistical hypotheses, regression and correlation. Requires a project. Prerequisite: MATH 331. Not available to students with credit for MATH 432 or MATG 630.
Topics include divisibility, prime numbers and their distribution, congruences, number theoretic functions, quadratic residues and nonresidues, Diophantine equations and primality testing. Optional topics may include elliptic curves and applications to cryptology. Prerequisites: MATH 372 or MATH 351.
Fundamental concepts in combinatorics including binomial coefficients, inclusion-exclusion, and generating functions. Topics in graph theory include connectivity, planarity, colorings and chromatic polynomials, and max-flow-min-cut in networks. Not open to students with credit for MATH 448 or CMPT 335. Prerequisites: MATH 243 and (MATH 372 or MATH 351).
The course covers the following topics: the growth of money, equations of value and yield rates, annuities certain, annuities with different payment and conversion periods, loan repayment, bonds, stocks and financial markets, arbitrage, term structure of interest rates, derivatives, and interest rate sensitivity. Prerequisite: Graduate status or permission of the graduate director.
Basic mathematical foundations and numerical techniques required to understand quantitative finance. Interest rates, hedging, Black Scholes formula, bootstrapping, finite differences and PDE's, Bond and portfolio optimization. Prerequisite: MATG 550.
Optimization, linear programming, simplex method, duality theory, transportation problems, scheduling problems, queuing theory. Students will be required to complete an independent project. The project will consist of a case study that will require researching a particular area of application, and then formulating, solving, and analyzing an appropriate mathematical model. Findings will be presented at the end of the term. Not open to students with credit for MATH 455. Prerequisite: MATH 372 or MATH 351.
Methods for solving non-linear optimization problems. Topics include unconstrained optimization, convex sets, approximation methods, method of least squares, convex programming, penalty methods and mixed constraints. Prerequisite: Graduate status or permission of the graduate director.
An introduction to the field of machine learning and its real-world applications, emphasizing the coding of machine learning algorithms to iteratively learn from data and to automate analytical model building. Topics include supervised & unsupervised learning, Bayesian decision theory, non-parametric methods, linear discriminant functions, multi-layer neural networks, stochastic methods and cluster analysis. Programming experience will be useful. A project is required. Not open to students with credit for MATH 457. Prerequisites:(MATH 372 or MATH 351) and MATH 285 and (MATH 331 or MATH 336).
Basic concepts of data mining. Fundamental aspects and techniques of analyzing large, complex data-sets. Topics include data objects and attributes, measuring data similarity, data visualization, data processing, apriori algorithm, classification methods, cluster analysis, outlier identification. Prerequisite: MATG 511.
A survey of the fundamental concepts in point set topology: open and closed sets, topological spaces, homeomorphisms, metric spaces, connectedness and compactness, illustrated by examples from applications in other disciplines. After a survey of the basics, the course will cover selected topics such as homotopy theory, chaos, fixed point theory, knots, manifolds and cosmology. Prerequisites: MATH 243 and MATH 387, with a grade of B or better, or equivalent.
MATG 571Advanced Linear Algebra with Applications3
A continuation of topics introduced in Linear Algebra (MATH 372), covering factorization of matrices. eigenvalues and eigenvectors, orthogonality, optimization problems, ill-conditioned matrices, applications to topics such as least-squares approximation, difference and differential equations, linear programming, networks, game theory. Prerequisite: MATH 372 or equivalent.
An introduction to algebraic structures with an emphasis on the theory of groups, subgroups, isomorphism, normal subgroups, cosets. Lagrange's theorem and the fundamental homomorphism theorems. This is a prerequisite course for graduate study in mathematics for students who lack the undergraduate background.
A rigorous treatment of differential calculus of one variable: sequences, limits, continuity, the derivative, the Riemann Integral. This is a prerequisite course for graduate study in mathematics for students who lack the undergraduate background.
Review of Riemann integral and the major theorems of integration in calculus. Review of infinite Series. Sequences and series of functions and their convergence properties, focusing on uniform convergence as the central notion for developing the properties of function spaces of real valued maps, such as equicontinuity and the Stone-Weierstrass theorem. Power series, Fourier series and the Gamma function. A rigorous definition of multivariable calculus using linear maps and differential forms over Rᵑ. Implicit and Inverse Function theorems and the general Stokes theorem. Introduction to Measure Theory. Prerequisites: MATH 387 or MATG 587.
Basic theorems in probability, random variables, distribution functions, expected values; binomial, Poisson and normal distributions. Sampling distributions, point estimation, interval estimation, testing statistical hypotheses. Prerequisite: MATH 331.
This is a data intensive course on statistical inference. Topics covered in this course include regression analysis, hypothesis testing, analysis of variance and nonparametric modeling. Students will utilize appropriate software for data analytics. Prerequisite: MATH 432 or MATG 630.
An introduction to probability models including random variables, conditional probability and expectation, Markov chains and time series. Additional topics may include Poisson processes, continuous time Markov processes, queueing theory, spatial, text and network models. Prerequisite: MATH 331 or MATG 630 or equivalent.
A course on the statistical foundations of machine learning, this course develops the fundamental ideas of statistical learning for drawing conclusions from multivariate data sets using statistical theory and applied linear algebra. The course combines a theoretical presentation with computation of the resulting machine-learning algorithms on real data sets to develop intuition of both how the methods work and how they perform in practice. It will cover the major techniques and concepts for both supervised and unsupervised learning. Topics will include regression, classification, resampling methods, model selection, regularization, principal components and clustering. Optional selected topics include tree-based methods, support vector machines and neural networks. Prerequisite: MATG 630.
Provides students with an in-depth understanding of the design, implementation and management of SQL, transactional database design, normalizing tables, functional dependencies, entity-relationship and relational database models; use of object-oriented design and event programming. Additional topics may include data warehouse modeling, analytics database systems, administration, security and other topics as time and interest permit. Prerequisite: CMPT 101 or equivalent.
Study of algebraic structures, such as rings, fields and integral domains, further study in group theory, applications of abstract theory. The course requires a written project. Prerequisite: MATH 377 or MATG 577.
This course focuses on the complex plane, complex functions, limits and continuity, as well as analytic functions, the Cauchy-Riemann equations, the Cauchy integral theorem, and consequences. Additional topics may include: power series, Taylor and Laurent series, classification of singularities, the residue theorem and its applications, conformal mapping, and selected applications. This course will require a written project and an oral presentation on some particular application of, or historical development in complex analysis. Not open to students with credit for MATH 490. Prerequisite: MATH 387.
Topics in Applied Mathematics. Offered in response to interests and needs of faculty and students. Can be repeated for credit. Prerequisite: Permission of the graduate director.
Topics in Mathematics. Offered in response to interests and needs of faculty and students. Can be repeated for credit. Prerequisite: Permission of the graduate director.
Students will receive guidance in securing an appropriate internship and must obtain faculty sponsorship. Faculty supervisors will define appropriate academic activities in parallel to the work requirement in order to provide a complete internship experience. Prerequisite: Permission of the graduate director.
Investigation of a research topic in mathematics culminating in a written paper and oral presentation. Prerequisite: Permission of the graduate director. Course may be taken more than once with different topics listed.
