Spring 2023 - ENSC 813 G100
Deep Learning Systems in Engineering (3)
Class Number: 2959
Delivery Method: In Person
Overview
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Course Times + Location:
Jan 4 – Apr 11, 2023: Tue, Thu, 2:30–4:20 p.m.
Burnaby -
Exam Times + Location:
Apr 20, 2023
Thu, 3:30–6:30 p.m.
Burnaby
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Prerequisites:
MATH 251 or ENSC 280 or ENSC 380 or permission of instructor.
Description
CALENDAR DESCRIPTION:
Covers machine learning basics, generalization theory, training, validation and testing. Introduces artificial neural networks, feedforward networks, convolutional networks, and types of layers in deep models. Provides overview of hardware architectures for deep learning: architectural and memory calculations; regularization and optimization of deep learning models. Analyzes recurrent and discursive networks. Culminates in a major project focusing on engineering applications of deep learning in signal processing, communications, biomedical engineering, robotics, or other areas. Students with credit for CMPT 880 - Special Topics in Computing Science: Deep Learning may not take this course for further credit.
Materials
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:
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Registrar Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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