Spring 2023 - CMPT 733 G200
Professional Master's Program Lab II (6)
Class Number: 6970
Delivery Method: In Person
The second of two lab courses that are part of the professional master's program in the School of Computing Science. Aims to provide students with experience needed for a successful career in the information technology industry. Students will learn core concepts of artificial intelligence, applied data science, and system and network security. Specifically, this includes data analytics, advanced statistics and data visualization, deep learning, and anomaly detection.
Lab practices, combined with instructional offerings, for students to acquire the hands-on experience necessary for a successful career in Visual Computing in the information technology sector. Topics covered will include fundamental and prevalent problems from application domains in the fields of computer graphics, computer vision, human-computer interaction, medical image analysis, as well as visualization.
Students will receive hands-on experience in deep learning, vision, image processing, and graphics, including convolutional neural networks, image stylization, image inpainting, image generation, deep learning for point clouds, Generative Adversarial Networks, etc. Guided labs teach students to exploit these algorithms to build prototype programs for real industrial applications.
COURSE-LEVEL EDUCATIONAL GOALS:
Learning modern visual computing algorithms with special focus on deep learning.
- It will be announced to students in the class. 100%
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html