ENDG319
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Probability, Statistics and Machine Learning
Schulich School of EngineeringEN - Schulich School of Engineering
Subject
ENDG - Digital Engineering
Description
Presentation and description of data, introduction to probability theory, Bayes' theorem, discrete and continuous probability distributions, estimation, sampling distributions, tests of hypotheses on means, variances and proportions; Introduction to fundamental machine learning including linear regression, classification and correlation. Applications are chosen from engineering practice from all disciplines.
Prerequisite(s): Mathematics 277 or 331; and one of Engineering 233, Digital Engineering 233 or 440.
Antirequisite(s): Credit for Digital Engineering 319 and Biomedical Engineering 319 will not be allowed.
Also known as: (formerly Engineering 319)
Prerequisite(s): Mathematics 277 or 331; and one of Engineering 233, Digital Engineering 233 or 440.
Antirequisite(s): Credit for Digital Engineering 319 and Biomedical Engineering 319 will not be allowed.
Also known as: (formerly Engineering 319)
GFC Hours
(3-1.5T)
Domestic Fee Rate Group
D
International Fee Rate Group
C
Signature Learning
Research & Creative Scholarship
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
Component
TUT
Units
3
Repeat for Credit
No