ENEL682
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Applied Machine Learning and Predictive Analytics
Subject
ENEL - Electrical Engineering
Description
Supervised, unsupervised, and semi-supervised machine learning. Classification, regression, clustering and generative models. Data analysis foundations including data matrix from algebraic and probabilistic view, numeric attributes, graph data, high dimensional data and dimensionality reduction, experimental setups, and quantitative metrics. Algorithms: traditional machine learning (e.g., random forests), neural networks, and deep learning. Hands-on industrial applications including signal classification, de-noising, anomaly detection, and predictive analytics.
Prerequisite(s): Admission to the MEng (course-based) program.
Antirequisite(s): Credit for Electrical Engineering 682 and 645 will not be allowed.
Prerequisite(s): Admission to the MEng (course-based) program.
Antirequisite(s): Credit for Electrical Engineering 682 and 645 will not be allowed.
Course Attributes
Fee Rate Group(Domestic) - B, Fee Rate Group(International) -D, GFC Hours (3-2)
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
LAB
Component
LEC
Units
3
Repeat for Credit
No
Subject code
ENEL