DATA607
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Statistical and Machine Learning
Mathematics and Statistics
SC - Faculty of Science
Subject
DATA - Data Science
Description
Latent variable models for clustering and dimension reduction. Parametric and nonparametric methods for regression and classification including naïve Bayes, decision trees, random forests, and boosting. Model assessment and selection. Deep learning.
Prerequisite(s): Data Science 601, 602, 603 and 604, and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science or Financial and Energy Markets Data Modelling.
Prerequisite(s): Data Science 601, 602, 603 and 604, and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science or Financial and Energy Markets Data Modelling.
Course Attributes
Fee Rate Group(Domestic) - H, Fee Rate Group(International) -G, GFC Hours (3-0)
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
DATA
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.