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MTHE 457 Statistical Learning Units: 3.00
Introduction to the theory and application of statistical algorithms. Topics include classification, smoothing, model selection, optimization, sampling, supervised and unsupervised learning.
(Lec: 3, Lab: 0, Tut: 0)
(Lec: 3, Lab: 0, Tut: 0)
Requirements: Prerequisites: MTHE 351 or equivalent
Corequisites:
Exclusions:
Offering Term: W
CEAB Units:
Mathematics 12
Natural Sciences 0
Complementary Studies 0
Engineering Science 24
Engineering Design 0
Offering Faculty: Smith Engineering
Course Learning Outcomes:
- Understand regression problems and algorithms.
- Understand the bias variance trade-off.
- Understand classification problems and algorithms.
- Perform supervised learning tasks on real-world datasets.
- Understand concepts in unsupervised learning problems.