ECE525
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ECE525 - Introduction to Deep Learning: An Engineering Perspective
Course Description
Deep Learning is revolutionizing artificial intelligence tasks such as language understanding, speech and image recognition, machine translation, autonomous driving, etc. This transformative impact of deep learning, which tries to model the neural networks in brains, was recognized with Nobel Prizes in 2024 and Turing Award in 2018. This course provides a comprehensive introduction to deep neural networks with a focus on underlying principles and engineering applications. Students will explore the fundamental concepts, optimization techniques, and software tools of deep learning starting from the basics of perceptron and progressing to advanced neural network models with convolutions and attentions. The course emphasizes an engineering perspective, hands-on learning, and integrating theory with practice, The course also introduces latest methods to enhance the efficiency of training and inference in deep learning models and systems. Designed for students from diverse engineering disciplines, this course aims to equip them with the skills and knowledge to effectively apply deep learning in their respective fields.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Requisites
Prerequisites: ECE 201, ECE 310, MATH 125 or MATH 122b, or equivalent experiences.
May be convened with
ECE425
Component
Lecture
Optional Component
No
Typically Offered Main Campus
Fall
Typically Offered Distance Campus
Not Offered
Typically Offered Online Campus
Not Offered
Typically Offered Phoenix Campus
Not Offered
Typically Offered Sierra Vista Campus
Not Offered
Typically Offered Community Campus
Not Offered