BIO 564
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Machine Learning for Biologists
Biology
College of Life Sciences
Course Description
Students will learn concepts and applications of machine-learning techniques applied to diverse types of biological data.
When Taught
Fall
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Note
This course is intended for undergraduate students and graduate students. Students who have not taken the prerequisite may contact the instructor for consent to take the course.
Title
Fundamental concepts
Learning Outcome
Students will be able to explain fundamental concepts of machine learning, describe key differences between machine-learning approaches, and identify common machine learning terminology and concepts used in biological applications.
Title
Data processing
Learning Outcome
Students will be able to describe and apply data preprocessing and feature engineering techniques to biological data.
Title
Supervised learning
Learning Outcome
Students will be able to describe the principles of supervised learning and its applications in biological classification and regression tasks and apply these techniques.
Title
Unsupervised learning
Learning Outcome
Students will be able to describe the principles of unsupervised learning and its applications in biological data clustering and dimensionality reduction and apply these techniques.
Title
Neural networks
Learning Outcome
Students will be able to implement and train neural networks for biological research tasks.
Title
Responsible use
Learning Outcome
Students will be able to describe and apply strategies to mitigate bias and ensure responsible machine learning practices in biological applications.
Title
Primary literature
Learning Outcome
Students will be able to critically evaluate primary literature on machine learning related to diverse biological subfields, including genomics, ecology, and medicine.
