COM SCI C121
Probabilistic Models in Computational Genomics
Description: (Formerly numbered CM121.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Civil and Environmental Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Prior knowledge of biology is not required. Designed for engineering students as well as students from biological sciences and medical school. Introduction to probabilistic models in the context of genomics, with emphasis on concepts and inventing new computational and statistical techniques to analyze genomic data. Concurrently scheduled with course C221. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2025 - Professor Pimentel is the GOAT. He gave lectures in a very very very engaging way and is very easy in terms of leaving doable and interesting assignments. Involves a lot of maths and stats in this class, but are all super helpful in terms of understanding RNA seq and underlying algorithms. He put a lot of effort into teaching this class and have very well prepared material. Although I would appreciate he goes beyond RNA sequencing and maybe go through other stuff like proteomics data. But he's exams and HWs are all pretty fair and he offers extra credit opportunities that worth 5% of your grade in total. His OH are super helpful and he is doing a wonderful job in explaining concepts during class. I would definitely recommend everyone taking this class, especially if you're doing RNA seq related research work. I honestly wish he will be teaching more lectures in the future cuz it's such an honor to take the class with such responsible and helpful professor.
Fall 2025 - Professor Pimentel is the GOAT. He gave lectures in a very very very engaging way and is very easy in terms of leaving doable and interesting assignments. Involves a lot of maths and stats in this class, but are all super helpful in terms of understanding RNA seq and underlying algorithms. He put a lot of effort into teaching this class and have very well prepared material. Although I would appreciate he goes beyond RNA sequencing and maybe go through other stuff like proteomics data. But he's exams and HWs are all pretty fair and he offers extra credit opportunities that worth 5% of your grade in total. His OH are super helpful and he is doing a wonderful job in explaining concepts during class. I would definitely recommend everyone taking this class, especially if you're doing RNA seq related research work. I honestly wish he will be teaching more lectures in the future cuz it's such an honor to take the class with such responsible and helpful professor.
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