CH EN 426
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Machine Learning for Engineers
Course Description
Machine Learning for Engineers combines mathematical details with several case studies that provide an intuition for machine learning with methods for classification, regression, and dimensionality reduction. A second phase of the course is a hands-on group project. The engineering problems and theory guide the student towards a working fluency in state-of-the-art methods implemented in Python.
When Taught
Winter Even Years
Fixed/Max
3
Fixed
3
Fixed
0
Other Prerequisites
Or equivalent Python programming course
Title
Understand relationships and assess data quality
Learning Outcome
Students will be able to visualize data to understand relationships and assess data quality.
Title
Create machine learning algorithms
Learning Outcome
Students will be able to apply linear algebra, statistics, and optimization techniques to create machine learning algorithms.
Title
Plan applications to achieve engineering and business objectives
Learning Outcome
Students will understand engineering and business objectives to plan applications.
Title
Assess data information content and predictive capability
Learning Outcome
Students will be able to assess data information content and predictive capability.
Title
Detect overfitting and improve prediction
Learning Outcome
Students will be able to detect overfitting and implement strategies to improve prediction.
