Course Descriptions

This course will cover topics in probability and statistics not discussed in other courses. Possible subjects include: large deviation theory, stochastic calculus and Ito’s formula, stochastic coupling and convergence rates, asymptotic techniques in probability and statistics, and advanced Markov chain algorithms.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): Permission of the instructor.
Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.

This course will cover topics in using computers to solve statistical problems. Possible subjects include: computational methods/toolkits for data wrangling, exploration, visualization and analysis with R/Python; R/python for data science; computational techniques (e.g. optimization, integration, algebra) for statistical inference; computing intensive statistical methods (e.g. bootstrapping methods, sample-size determination, Monte Carlo methods).

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): Permission of the instructor.
Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.

This course will cover topics in Applied Statistics not discussed in other courses. Possible subjects include: exploratory data analysis, survival analysis, longitudinal data analysis, spatial statistics, non-parametric methods, and data mining and visualization.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 344.3 or permission of the instructor.
Note: Students may take this course more than once for credit provided that the topics covered in each offering differ substantially. Students must consult the Department to ensure that the topics covered are different.

Parametric estimation, maximum likelihood estimators, unbiased estimators, UMVUE, confidence intervals and regions, tests of hypotheses, Neyman Pearson Lemma, generalized likelihood ratio tests, chi-square tests, Bayes estimators.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): STAT 342.

A rigorous examination of the general linear model using vector space theory. Includes: generalized inverses; orthogonal projections; quadratic forms; Gauss-Markov theorem and its generalizations; BLUE estimators; Non-full rank models; estimability considerations.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 164 (formerly MATH 264) or MATH 266, STAT 342, and STAT 344 or 345.

Based on a foundation of mathematical and statistical theory, the course covers a series of statistical methods for supervised learning and unsupervised learning, focusing on applications to real data using statistical software. The topics include: resampling methods such as Cross-Validation and Bootstrap; regression and classification including Linear Regression, Logistic Regression, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN); model selection and regularization including Best Set Selection, Lasso, Elastic Net; non-linear models including Generalized Additive Models (GAM); tree-based methods including Decision Trees, Bagging, Random Forest; Support Vector Machines (SVM); dimension reduction and clustering including Principle Component Analysis (PCA), K-Means, Hierarchical Clustering; Ensemble Learning including Boosting, Stacking, Multi-Task Prediction; and an introduction to Deep Learning.

Weekly hours: 3 Lecture hours and 1.5 Practicum/Lab hours
Prerequisite(s): STAT 344.3 or STAT 345.3 or CMPT 317.3 or CMPT 318.3
Note: Students with credit for STAT 498.3 Machine Learning or STAT 847 may not take this course for credit.

The multivariate normal distribution, multivariate analysis of variance, discriminant analysis, classification procedures, multiple covariance analysis, factor analysis, computer applications.

Weekly hours: 3 Lecture hours and 1 Practicum/Lab hours
Prerequisite(s): MATH 164 (formerly MATH 264) or MATH 266, STAT 241, and one of STAT 344 or STAT 345.
Note: Students with credit for STAT 346.3 may not receive credit for this course.

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Seminar/Discussion hours

Offered occasionally by visiting faculty and in other special situations to cover, in depth, topics that are not thoroughly covered in regularly offered courses.

Weekly hours: 3 Seminar/Discussion hours