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This course suits bioinformatics and data science tracks. It develops the student conceptual and applied insights to extract knowledge from biomedical datasets and tackle known modeling and data challenges. The course covers two main parts: most traditional machine learning methods and deep learning essentials. Although the course teaches the fundamental mathematics and statistical aspects behind each learning algorithm, it emphasizes on how to develop and validate baseline and optimal models for practical biomedical applications. During the processes of developing and validating models, students will learn to interpret results properly and conduct an effective error analysis. Students will learn how to use the error analysis results to tune the learning algorithms for mitigating modeling and data challenges.
This noncredit class meets with a regular University of Utah credit course.