Subject: Statistics
Credit units: 3
Offered: Either Term 1 or Term 2
Weekly hours: 3 Lecture hours and 1.5 Practicum/Lab hours
College: Arts and Science
Department: Mathematics and Statistics


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.

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.

Upcoming class offerings

For full details about upcoming courses, refer to the class search tool or, if you are a current student, the registration channel in PAWS.


The syllabus is a public document that provides detail about a class, such as the schedule of activities, learning outcomes, and weighting of assignments and examinations.

Once an instructor has made their syllabus publicly available on USask’s Learning Management System, it will appear below. Please note that the examples provided below do not represent a complete set of current or previous syllabus material. Rather, they are presented solely for the purpose of indicating what may be required for a given class. Unless otherwise specifically stated on the content, the copyright for all materials in each course belongs to the instructor whose name is associated with that course. The syllabus is the intellectual property of instructors or the university.

For more information, visit the Academic Courses Policy , the Syllabus page for instructors , or for students your Academic Advising office.