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Machine Learning Applications in Genetics
(Same as Human Genetics CM124.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, Mathematics 33A, and one course from Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Designed for engineering students as well as students from biological sciences and medical school. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science. Concurrently scheduled with course CM224. Letter grading.
Review Summary
- Clarity
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8.3 / 10
- Organization
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8.3 / 10
- Time
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5-10 hrs/week
- Overall
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8.3 / 10
Reviews
Professor was a really good lecturer just material and length of lecture can make it boring. Professor obviously cares about teaching
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Course
Grading Information
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No group projects
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Attendance not required
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1 midterm
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Finals week final
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0% recommend the textbook
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