Preliminary Fall / Winter 2021-2022 Timetable

If you are planning to enrol in a graduate course at the Department of Statistical Sciences, we recommend you read through this page carefully. Please also note, that this course schedule is subject to changes. We will post changes and updates here, so check back frequently.

Enrolment Dates

Students enrolled at our department

Registration for the Fall Session starts at 6:00 am EST on Tuesday, July 20, 2021 for students enroled at the Department of Statistical Sciences only.

Students from other departments

Course enrolment for students from other department will start on Tuesday, August 24, 2021. You will not be able to add courses before this date. To enrol in one of our graduate courses, you will have to...

If you have any additional questions, please contact the graduate team.

Start and End Dates of Classes & Final Examination Period

Term Classes Start Classes End

Fall Session 2021
(cross-listed courses)

Thursday, September 9, 2021 Wednesday, December 8, 2021 (Dec 9, 2021 Make-up Day)

Fall Session 2021
(graduate courses, not cross-listed)

Thursday, September 9, 2021 Wednesday, December 8, 2021 (Dec 9, 2021 Make-up Day)

Winter Session 2021
(all courses)

Monday, January 10, 2022

Friday, April 8, 2022

 

At this point in time, the Faculty of Arts & Science and the University of Toronto are optimistic that most courses, student services and co-curricular activities will be able to proceed in person, with the possible exception of large-scale gatherings. Please see UTogether for more information on the University of Toronto’s plans for Fall 2021.

For dates regarding university closures, course drop and registration deadlines, and tuition payment deadlines, please have a look at the School of Graduate Studies sessional dates calendar.

Course List Legend

  • F = a half-year course in the first term (September – December)
  • S = a half-year course in the second term (January– April)
  • Y = a full-year course (September – April)
  • (J) indicates a a cross-listed course (a joint graduate/undergraduate course)
  • M = Monday
  • T = Tuseday
  • W = Wednesday
  • R = Thursday
  • F = Friday
  • L0101 or L0201 = 9:00 am to 5:00 pm
  • L5101 = 5:00 pm onwards

Fall / Winter 2021-22 Timetable Course Listings

The Fall / Winter 2021-22 Timetable consists of two sessions: the Fall Session 2021 and the Winter Session 2022. Please find course listings for both sessions below. You can also find a list of our graduate courses at the School of Graduate Studies page for our department.

 

 

Fall Session 2021: Course Listings

Course Title (Click for description) Session Section/Time Location Instructor Delivery Method

STA1008H

Vocabulary of data analysis, Tests of statistical significance, Principles of research design, Introduction to unix, Introduction to SAS, Elementary significance tests, Multiple regression, Factorial ANOVA, Permutation tests, Power and sample size, Random effects models, Multivariate analysis of variance, Analysis of within-cases designs (repeated measures). If time permits, Categorical data analysis.

Prerequisite: Any introductory statistics class, taught by any department.

F
L0101: T10-12; F10
TBA TBA

STA2047H

A rigorous introduction to stochastic analysis and its applications. Topics include Brownian motion, continuous time martingale, stochastic integration, stochastic differential equations, diffusions, and further topics depending on the interests of the instructor.

Prerequisite: No explicit prerequisites, but to understand the material, it is necessary to have a good understanding of real analysis and probability theory at the graduate level.

Course credit: 0.5 FCE

F
L0101: M10-12; W11
 TBA

Wong, Leonard

TBA

STA2052H

Modern statistical methods and data analytics are increasingly informing decisions in law, business, medicine, and public life. While the use of statistics to understand social problems is not new, its pervasiveness in society and the scale of available data available opens up a host of new and/or salient moral problems including, for example, fairness, bias, privacy, equality, transparency, accountability, and accessibility.

In this course, we will combine material from law and philosophy together with recent work in statistics and data science in order to gain a better understanding of how to intelligibly reason about these problems, and how to responsibly and creatively apply statistical methods to complex social problems.

The course will be research/project based and the emphasis will be on using statistics to address complex social problems rather than on memorizing abstract ethical principles for handling or processing data.

Prerequisite: Graduate students should have an adequate background in probability and statistics (including the use of R), equivalent to two undergraduate courses in the field. Familiarity with Bayesian approaches and statistical learning/classification would also be helpful. No formal prerequisites.

Course credit: 0.5 FCE

F

L5101: W6-9

TBA

Babic, Boris

TBA

STA2101H

This course will focus on principles and methods of applied statistical science. It is designed for MSc and PhD students in Statistics, and is required for the Applied Paper of the PhD comprehensive exams.  The topics covered include: planning of studies, review of linear models, analysis of random and mixed effects models, model building and model selection, theory and methods for generalized linear models, and an introduction to nonparametric regression. Additional topics will be introduced as needed in the context of case studies in data analysis.

Prerequisite:

  • ECO374H1/ECO375H1/STA302H1
  • STA305H1
F

L0101: W10-1

TBA Reid, Nancy TBA

STA2111H

STA 2111H is a course designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: probability measures, the extension theorem, random variables, distributions, expectations, laws of large numbers, Markov chains.

Students should have a strong undergraduate background in Real Analysis, including calculus, sequences and series, elementary set theory, and epsilon-delta proofs. Some previous exposure to undergraduate-level probability theory is also recommended.

F L0101: R10-1 TBA Zhou, Zhou TBA

STA2112H

This course is designed for graduate students in Statistics and Biostatistics.

Review of probability theory, distribution theory for normal samples, convergence of random variables, statistical models, sufficiency and ancillarity, statistical functionals,influence curves, maximum likelihood estimation, computational methods.

Prerequisite:

  • Advanced calculus (eg. MAT237)
  • Linear algebra (eg. MAT223, MAT224).
  • A previous course in probability and/or statistics is highly recommended.
F L0101: T10-1

TBA

Gronsbell, Jessica TBA

STA2163H

This course presents mathematical foundations for learning, prediction, and decision making. Unlike in traditional statistical learning, however, our focus will be on notions of optimality that do not rely on stochastic modeling assumptions on data. A primary focus will be on learning from data to compete with a class of baselines predictors / strategies, often referred to as experts. A secondary focus will be on the ability to adapt to the presence or absence of statistical patterns, without presuming at the outset that such patterns will arise. Topics include: regret; prediction with expert advice; the role of the loss function in tight bounds; online classification; online linear and convex optimization; regularization; bandit problems / decisions with limited feedback; minimax optimality and adaptivity; relationships with statistical learning.

Prerequisite: Mathematical maturity, including real analysis, linear algebra, and probability theory.

Course credit: 0.5 FCE

F
L0101: W2-5
TBA TBA

STA2453H

This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (September-December) and winter (January-April). The overall workload is approximately equivalent to a half graduate course and students receive a half credit.

Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants.

Content: There is some classroom instruction at the start of the term, an d meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times.

Evaluation: Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients.

Prerequisite: Students should have taken some applied sta tistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work.

Y

L0101: T10-12

TBA

Murray, Josh TBA

STA2501H

Consult the instructor for further details.

Prerequisite: Consult the instructor concerning necessary background for this course

F TBA TBA TBA TBA

STA2555H

(CSC2537H)

In this course we will study techniques and algorithms for creating effective data visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science.This course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

F TBA TBA TBA TBA

STA2700H

This is a reading course primarily meant to sequentially follow a modular course offered in the Department. Its purpose is to offer further supervised study of an advanced topic covered for the ambitious student.

F L0101: T9-11; R11 TBA Molkaraie, Mehdi TBA

STA3000Y

Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences.

This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings.

Prerequisite:

  • STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be co-requisites).
  • Some familiarity with measure theory is very useful. The text includes some supplementary material on this.
F L0101: T10-1 TBA Wang, Linbo/Kong, Dehan TBA

STA3431H

This course will explore Monte Carlo computer algorithms, which use randomness to perform difficult high-dimensional computations. Different types of algorithms, theoretical issues, and practical applications will all be considered. Particular emphasis will be placed on Markov chain Monte Carlo (MCMC) methods. The course will involve a combination of methodological investigations, mathematical analysis, and computer programming.

Prerequisite: Knowledge of statistical inference and probability theory at the advanced undergraduate level, and familiarity with basic computer programming techniques.

F L0101: M10-12 TBA Rosenthal, Jeffrey TBA
STA4526H

The course will introduce students to the basic theory of stochastic optimal control. We will cover both the analytic approach, including an introduction to viscosity solution theory, and the probabilistic approach which is based on BSDE and the stochastic maximum principle. Applications to portfolio optimization and contract theory will be discussed. Prerequisite to this course include (measure-theoretic) probability theory and stochastic calculus.

Prerequisite: (measure-theoretic) probability theory and stochastic calculus

Course credit: 0.25 FCE

F (First Half of Semester) L0101: M1-4 TBA Zhang, Yuchong TBA
STA4529H

Nonstandard analysis provides a rigorous foundation for carrying out mathematical analysis with the aid of infinitesimal numbers and other structures that appear in so-called saturated models of the real numbers. This course introduces nonstandard analysis using concepts and examples from statistics and probability. Topics include: extension, transfer, and saturation; infinitesimal and infinite numbers; hyperfinite sets and measures; hyperfinite models of stochastic processes; nonstandard Bayesian decision theory and connections to frequentism. Background in real analysis, probability theory, and statistics recommended. No background will be assumed in mathematical logic.

Prerequisite: Mathematical maturity, including real analysis and probability theory. MSC with instructor approval.

Course credit: 0.25 FCE

F (TBC) TBC TBA Roy, Dan TBA

 

Winter Session 2022: Course Listings

Course Title (Click for description) Session Section/Time Location Instructor Delivery Method
STA2051H

Techniques for formulating data science models as optimization problems. Algorithms for solving data science problems including gradient-descent based algorithms and randomized algorithms.  Emphasis on scalability and efficiency. Convergence analysis of algorithms.  Coverage of both convex and nonconvex optimization.

Course credit: 0.5 FCE

S L0101: T2-4; R2 TBA Vavasis, Steve TBA

STA2201H

The course will focus on generalized linear models (GLM) and related methods, such as generalized additive model involving nonparametric regression, generalized estimating equations (GEE) and generalized linear mixed models (GLMM) for longitudinal data. This course is designed for Master and PhD students in Statistics, and is REQUIRED for the Applied paper of the PhD Comprehensive Exams in Statistics. We deal with a class of statistical models that generalizes classical linear models to include many other models that have been found useful in statistical analysis, especially in biomedical applications. The course is a mixture of theory and applications and includes computer projects featuring R (S+) or/and SAS programming.

Topics: Brief review of likelihood theory, fundamental theory of generalized linear models, iterated weighted least squares, binary data and logistic regression, epidemiological study designs, counts data and log-linear models, models with constant coefficient of variation, quasi-likelihood, generalized additive models involving nonparametric smoothing, generalized estimating equations (GEE) and generalized linear mixed models (GLMM) for longitudinal data.

Prerequisite: Advanced Calculus, Linear Algebra, STA 347 and STA 422 (upper-division courses on probability and statistical inference) or equivalent, STA 302 (linear regression), Statistical Computing using R (S+) or/and SAS (alternative softwares are allowed). However, please be advised that I may not be familiar with the software of your choice resulting in limited assistance.

S L0101: W10-1 TBA TBA

STA2211H

STA 2211H is a follow-up course to STA 2111F, designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: weak convergence, characteristic functions, central limit theorems, the Radon-Nykodym Theorem, Lebesgue Decomposition, conditional probability and expectation, martingales, and Kolmogorov’s Existence Theorem.

S L0101: R10-1 TBA Volgushev, Stanislav TBA

STA2212H

This course is a continuation of STA2112H. It is designed for graduate students in statistics and biostatistics.

Topics include:

  • Likelihood inference
  • Bayesian methods
  • Significance testing
  • Linear and generalized linear models
  • Goodness-of-fit
  • Computational methods

Prerequisite: STA2212H

S L0101: T10-1 TBA Reid, Nancy TBA

STA2453H

This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (September-December) and winter (January-April). The overall workload is approximately equivalent to a half graduate course and students receive a half credit.

Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants.

Content: There is some classroom instruction at the start of the term, an d meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times.

Evaluation: Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients.

Prerequisite: Students should have taken some applied sta tistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work.

Y L0101: T10-12 TBA Murray, Josh TBA

STA3000Y

Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences.

This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings.

Prerequisite:

  • STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be co-requisites).
  • Some familiarity with measure theory is very useful. The text includes some supplementary material on this.
S L0101: W10-1 TBA Zwiernik, Piotr TBA

STA4273H

This is a full semester course.

The problem of minimizing an expected value is ubiquitous in machine learning, from approximate Bayesian inference to acting optimally in a Markov decision process. Progress on this problem may drive advances in methods for generating novel images, unsupervised discovery of object relations, or continuous control. This course will introduce students to various methodological issues at stake in this problem and lead them in a discussion of its modern developments. Introductory topics may include stochastic gradient descent, gradient estimation, policy and value iteration, and variational inference. The class will have a major project component.
 
Prerequisites: This course is designed to guide students in an exploration of the current state of the art, so that ideally, their course projects can make a novel contribution. A previous course in machine learning such as CSC321, CSC411, CSC412, STA414, or ECE521 is strongly recommended. The only hard requirements are linear algebra, multivariate calculus, probability, and basic programming skills.

S L0101: W 2-4 TBA Butler, Kenneth TBA
STA4506H

The course will cover modeling, estimation and inference of non-stationary time series. In particular, we will deal with statistical inference of trends, quantile curves, time-varying spectra and functional linear models related to non-stationary time series. With the recent advances in various fields, a systematic account of non-stationary time series analysis is needed.

Course credit: 0.25 FCE

S (Second Half of Semester) L0101: R10-1 TBA Zhou, Zhou TBA
STA4508H

Inference based on the likelihood function has a prominent role in both theoretical and applied statistics.  This course will introduce some of the more recent developments in likelihood-based inference, with an emphasis on adaptations developed for models with complex structure or large numbers of nuisance parameters.  Special emphasis will be given to the theoretical and applied aspects of composite likelihood, and to the use of quasi-likelihood and generalized estimating equations. Tentative topics to be covered include: review of likelihood inference and asymptotic results; adjustments to profile likelihood; misspecified models — composition likelihood; partially specified models — quasi-likelihood; properties and limitations of penalized likelihood.

Course credit: 0.25 FCE

S (First Half of Semester) L0101: W2-5 TBA Reid, Nancy TBA
STA4512H

The general mathematics and logical foundations for statistical inference: geometric, algebraic and topological symmetries that arise naturally in the solution to the inference problem, including rigorous comparison of the bayesian and frequentist approaches, and the group theoretic considerations of invariance (algebraic and logical symmetry), both on the sample space as well as on the parameter space (and both either implicit or manifest) that must be taken into account in the analysis. Unusual for the development, but fundamental to the inherent logic of such considerations, the finite-finite case is given special attention in respect of both sample space and parameter space.

Course credit: 0.25 FCE

S (Second Half of Semester) L0101: M12-3 TBA Brenner, David TBA
STA4518H

This course will give an overview of robust statistical methods, that is, methods that are insensitive to outliers or other data contamination. Topics will include theoretical notions such as qualitative robustness and breakdown point, robust estimation of location (minimax variance and bias) and scale parameters, robust estimation in regression and multivariate analysis, and applications (including in computer vision).

Prerequisite:

  •     STA2112H
  •     permission

Course credit: 0.25 FCE

S (Second Half of Semester) L0101: T1-4 TBA Knight, Keith TBA

JAS1101H

This graduate-level course provides an introduction to the cross-disciplinary field of astrostatistics, and is intended for both astronomy and statistics students. We will cover topics in statistics (e.g., hierarchical Bayesian analysis, time series analysis, and cluster analysis) in the context of their applications to astronomical research (e.g., studies of galaxies, the Milky Way, exoplanets, and stellar populations).

These topics will be covered through two main aspects of the course: 1) peer-instruction and collaboration on a term project, and 2) readings, in-class discussion, and exercises related to current astrostats literature. For the term project, the students will develop practical skills by collaborating in cross-disciplinary teams on a research project in astrostatistics using real astronomical data.

S

L0101: T1-2:30; W3-4:30

TBA Eadie, Gwendolyn TBA