ENSF617
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Introduction to Machine Learning
Subject
ENSF - Software Engineering for Engineers
Description
Overview of the historical context that allowed deep learning to flourish. Different types of neural networks; how to train and deploy them in various problems; Fine-tuning pre-trained models to achieve state-of-the-art results in relevant applications; overview of the current trends in deep learning.
Prerequisite(s): Admission to the MEng with specialization in Software Engineering.
Antirequisite(s): Credit for Software Engineering for Engineers 617 and Electrical Engineering 619.51, 645 or 682 will not be allowed.
Prerequisite(s): Admission to the MEng with specialization in Software Engineering.
Antirequisite(s): Credit for Software Engineering for Engineers 617 and Electrical Engineering 619.51, 645 or 682 will not be allowed.
Signature Learning
Research & Creative Scholarship
Course Attributes
Fee Rate Group(Domestic) - B, Fee Rate Group(International) -D, GFC Hours (3-0), Research & Creative Scholarship - Related
Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.
Component
LEC
Units
3
Repeat for Credit
No
Subject code
ENSF