For more information on our course offerings, please go to York Course Website.
Calendar Year
Term
Course #
Course Title
2024
F
gs/math 5840A
Mathematics Learning Environments
Instructional Format: SEMR
Instructor(s): M. Osibodu
2025
W
gs/math 5900M
Thinking about Teaching Mathematics
This course invites participants to reflect on the practice of teaching mathematics in light of research, theory, and their own experiences. It examines how teachers draw on mathematical and pedagogical knowledge in their work. A specialised mathematics background is not a prerequisite.
Instructional Format: SEMR
Instructor(s): T. Rapke
2024
F
gs/math 5910A
Quantitative Research Methods in Education
Quantitative Research Methods in Education”/Same as Mathematics & Statistics 5910 3.0 Calendar course description: This course discusses theoretical, practical and ethical considerations, decisions and processes involved in conducting quantitative research in education. It explores the research process from formulating research questions, study design, sampling, data collection, analysis and interpretation, to communicating research processes and findings.
Instructional Format: SEMR
2025
W
gs/math 5920M
Research in Mathematics Education
Presently there are no research methods courses that focus on the particular needs of the students interested in doing research in mathematics education. This course is intended as a companion to the course in qualitative and quantitative methods.
Instructional Format: SEMR
Instructor(s): M. Osibodu
2024
F
gs/math 6001A
Survey Paper
Grading of a Survey Paper will follow the format of grading of other courses in the programme (i.e. A+, A, etc.). A student will normally be required to present his/her paper in a student Colloquium. The final grade will be partially dependent on the student’s performance during such a presentation.
Instructional Format: RESP
2025
W
gs/math 6001M
Survey Paper
Grading of a Survey Paper will follow the format of grading of other courses in the programme (i.e. A+, A, etc.). A student will normally be required to present his/her paper in a student Colloquium. The final grade will be partially dependent on the student’s performance during such a presentation.
Instructional Format: RESP
2024
F
gs/math 6002A
Directed Reading
Instructional Format: DIRD
Instructor(s): M. Omar
2025
W
gs/math 6002M
Directed Reading
Instructional Format: DIRD
2024
F
gs/math 6004A
Mathematics Seminar
Students are required to present two one-hour seminars under faculty supervision and to attend other students’ presentations. Written reports on the seminars are also required. The topics can be chosen from any field of mathematics but must be distinct from the material of the student’s coursework, thesis or survey paper.
Instructional Format: SEMR
2025
W
gs/math 6004M
Mathematics Seminar
Students are required to present two one-hour seminars under faculty supervision and to attend other students’ presentations. Written reports on the seminars are also required. The topics can be chosen from any field of mathematics but must be distinct from the material of the student’s coursework, thesis or survey paper.
Instructional Format: SEMR
2024
F
gs/math 6121A
Applied Algebra
Group theory and representation theory, preliminary notions in ring (Euclidian domain, principal ideal domain and polynomial rings), Grobner bases with some emphasis on algorithmic aspects and computational geometry, modules over a PID (linear algebra). Prerequisite: equivalent of undergraduate second-year linear algebra and some basic notions in algebra (group, ring), or permission of the Instructor.
Instructional Format: LECT
Instructor(s): Y. Gao
2025
W
gs/math 6122M
Algebra II
Introduction to category theory, additional ring and module theory, introduction to algebraic geometry, field theory and Galois theorems. Prerequisite: GS/MATH 6121 3.00 or permission of the Instructor.
Instructional Format: LECT
Instructor(s): P. Ingram
2025
W
gs/math 6280M
Measure Theory
s-algebras, generating classes, finite and s-finite measures on general spaces, constructing measures from outer measures, Caratheodory extension theorem, Jordan decomposition into variations. Hahn decomposition, measurable functions, Lusin’s theorem, Lebesgue integral on general spaces, convergence theorems in an abstract setting, Radon-Nikodym theorem, Riesz representation theorem, Maharam’s classification of measure algebras, product spaces, Fubini’s theorem, non-measurable sets, Lebesgue density theorem.
Instructional Format: LECT
Instructor(s): P. Skoufranis
2025
W
gs/math 6340M
Ordinary Differential Equations
Instructional Format: LECT
Instructor(s): H. Zhu
2024
F
gs/math 6350A
Partial Differential Equations
Fourier transforms, homogeneous distributions, singular integrals, spherical harmonics, pseudo-differential operators, fundamental solutions and parametrices of partial differential operators.
Instructional Format: LECT
Instructor(s): M. Wong
2024
F
gs/math 6461A
Functional Analysis I
An introduction to Banach and Hilbert spaces together with bounded linear functionals and operators on these spaces. Topics include: the Hahn-Banach theorem, representation of dual spaces, the uniform boundedness principle, the open mapping and closed graph theorems, compact operators.
A systematic presentation of many statistical concepts and techniques for the analysis of time series data. The core topics include time dependence and randomness, trend, seasonality and error, stationary process, ARMA and ARIMA processes, multivariate time series and state-space models. Integrated with the undergraduate course SC/MATH 4130B 3.00.
Instructional Format: LECT
Instructor(s): K. Zhao
2025
W
gs/math 6641M
Survival Analysis
This course provides students with an introduction to the statistical methods for analyzing sensored data which are common in medical research, industrial life-testing and related fields. Topics include accelerated life models, proportional hazards model, time dependent covariates. Integrated with the undergraduate course Mathematics & Statistics 4130K 3.0. Prerequisites: York undergraduate Mathematics 3033 3.0 and 3132 3.0 or equivalents, or permission of the instructor.
Instructional Format: LECT
Instructor(s): D. Sharma
2025
W
gs/math 6644M
Statistical Learning
Offers a comprehensive introduction to statistical learning theory including most widely used methods such as classification trees, support vector machine and model averaging. Prerequisites: GS/MATH 6620 3.00 and GS/MATH 6630 3.00 or equivalents or permission of the Instructor.
Instructional Format: LECT
Instructor(s): S. Wang
2024
F
gs/math 6650A
Introduction to Statistical Data Science
This course serves as an introduction to data science from the perspective of statistics. The course begins with an introduction of relevant computational and analytical tools necessary to analyze large data sets (with the understanding that the tools which are most relevant change over time) including Python, R and SAS. The course will then focus on visualisation tools and exploratory data analysis, high dimensional statistical tools such as LASSO and tensor analysis, as well as causality and propensity scores. The students will learn how these tools are implemented on large data sets through case studies.
Provides a rigorous treatment of numerical methods for the solutions of ordinary and partial differential equations.
Instructional Format: LECT
Instructor(s): M. Couchman
2025
W
gs/math 6655M
Feedback Control Systems
Instructional Format: TUTR
2024
F
gs/math 6902A
Stochastic Programming
Topics include stochastic programming modeling techniques and issues, stochastic programming theories, various solving methods: L-shaped method, Nested Decomposition method, Progressive Hedging method, Interior Point method, various scenario sampling scheme, and approximation methods.
Instructional Format: LECT
Instructor(s): M. Chen
2024
F
gs/math 6910A
Stochastic Calculus In Finance
Probability models and discrete time stochastic processes; Brownian motion; Filtrations, conditional expectations, martingales; Stochastic integrals, Ito’s formula; Stochastic differential equations; Diffusions, Kolmogorov equation; Girsanov Formula; Black Scholes.
Instructional Format: LECT
Instructor(s): J. Cao
2025
W
gs/math 6910M
Stochastic Calculus In Finance
Probability models and discrete time stochastic processes; Brownian motion; Filtrations, conditional expectations, martingales; Stochastic integrals, Ito’s formula; Stochastic differential equations; Diffusions, Kolmogorov equation; Girsanov Formula; Black Scholes.
Instructional Format: LECT
Instructor(s): H. Ku
2025
W
gs/math 6911M
Numerical Methods in Finance
Introduction to Partial Differential Equations; basic properties of the diffusion equation; Finite difference methods; explicit methods; implicit methods including Crank Nicholson; Free boundary problems; variational inequalities; Lattice Methods.
Instructional Format: LECT
Instructor(s): A. Kuznetsov
2025
W
gs/math 6912M
Machine Learning in Finance
This introductory course on machine learning is aimed at graduate students who are going to work with data scientists in the finance industry. The course explains the most popular machine learning algorithms used by data scientists, which enables the students to assess their strength and weakness for a particular application and work productively with data scientists. Algorithms are illustrated with different data sets from the finance industry and others in EXCEL and Python.
Instructional Format: LECT
Instructor(s): H. Zhu
2024
F
gs/math 6931A
Mathematical Modeling
Instructional Format: LECT
Instructor(s): S. Moghadas
2025
W
gs/math 6936M
Mathematical Epidemiology
This course will cover the basic tools required to critically read modelling papers and to develop and use models as research tools. Models of infectious disease; threshold conditions for epidemic outbreaks, the basic reproductive rate of a disease; vaccination strategies to control infection. Emphasis will be placed on setting up and utilizing mathematical models to understand infectious disease processes and to evaluate potential control strategies. This course will provide an opportunity for students who are interested in using mathematical modelling techniques to study the transmission dynamics of infectious disease on a population level. The course will help to develop modelling, analytical and computational expertise in both continuous time and discrete time dynamical models for the study of infectious diseases. Prerequisites: Atkinson/Arts/Science Mathematics & Statistics 2270 3.0: Differential equations or equivalent and some numerical skills or equivalent and familiarity with Maple or Matlab or Mathematica
Instructional Format: LECT
Instructor(s): W. Woldegerima
Learn More
The Graduate Program in Mathematics and Statistics at York is an exciting environment to pursue innovative, socially engaging, career-ready education. Contact our Graduate Program Assistant to learn more.