ENDG319
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Probability, Statistics and Machine Learning
Schulich School of Engineering
EN - 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): 3 units from Mathematics 277, Applied Mathematics 219 or Energy Engineering 240; and 3 units from Engineering 233, Digital Engineering 233 or Digital Engineering 440.
Antirequisite(s): Credit for Digital Engineering 319 and Biomedical Engineering 319 will not be allowed.
Also known as: (formerly Engineering 319)
Prerequisite(s): 3 units from Mathematics 277, Applied Mathematics 219 or Energy Engineering 240; and 3 units from Engineering 233, Digital Engineering 233 or Digital Engineering 440.
Antirequisite(s): Credit for Digital Engineering 319 and Biomedical Engineering 319 will not be allowed.
Also known as: (formerly Engineering 319)
Course Attributes
GFCH - 3-1.5T ((3-1.5T))
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
Subject code
ENDG
Understanding Course Information
Please refer to Course Terminology and Description to better understand how to interpret course information such as GFC Hours, pre-requisites, course levels, etc.