Subject: Computer Science
Credit units: 3
Weekly hours: 3 Lecture hours
College: Arts and Science
Department: Computer Science

A survey of Deep Learning techniques and their application to problems in computer vision and data science. Deep learning techniques may include Deep Neural Networks, Convolutional Neural Networks, Recurrent Networks, Deep Generative Models and Reinforcement Learning. Application domains will focus on computer vision problems, including image classification, object detection and image segmentation. Additional application domains in natural language processing and robotics control will be introduced. Software tools will be introduced for practical application.

Prerequisite(s): One of MATH 164.3, MATH 266.3, EE 216.3, or CE 318.3; and one of STAT 242.3 (preferred) or STAT 245.3; and one of CMPT 317.3 or CMPT 487.3
Note: Students with credit for CMPT 828 or CMPT 498.3 Deep Learning and Applications may not take this course for credit.

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

• Class search
• Registration channel

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.

• Bookstore Textbook Search
• Library Reserves Search
• USask Tutoring Network