Algorithms in Bioinformatics
(Same as Chemistry CM160B.) Lecture, four hours; discussion, two hours. Requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Course CM121 is not requisite to CM122. Designed for engineering students as well as students from biological sciences and medical school. Development and application of computational approaches to biological questions, with focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. Computational techniques include those from statistics and computer science. Concurrently scheduled with course CM222. Letter grading.
Review Summary
- Clarity
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1.7 / 10
- Organization
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1.7 / 10
- Time
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5-10 hrs/week
- Overall
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1.7 / 10
Reviews
Extremely high amount of work, extremely easy to get an A. Extremely hard to pay attention to lecture presentations, extremely good textbook explanations. Highest amount of work I have ever done for a class, easiest exams I have ever had for a class.
The course was recorded but access was limited to students with a valid excuse like illness or an accident, they wanted full attendance yet most students ended up working on their laptops during class. Professors Eskin and Ernst led the instruction; Eskin often added humor to his lectures which I really enjoyed, while Ernst had a vast knowledge but his monotone delivery was a bit sleep-inducing. This was a class where sitting at the front and asking questions when things got confusing really helped; the professors were always ready to engage and clarify.
The exams were quite similar to the practice materials and the TA was a great help during discussion sections. The projects were a different story, they were tough mostly because it wasn’t clear what was expected until just days before they were due. We had four projects throughout the quarter and regular homework assignments submitted on a platform called Stpeik.
The class touched on concepts relevant to a variety of fields. For instance, gene clustering helped demonstrate the pros and cons of different methods and diving into Hidden Markov Models turned out to be unexpectedly enjoyable.
In a nutshell, if you’re a committed computer science student with solid coding skills, especially if you remember what you learned in CS 32 and 180, you should do well in this class. Some students in the class GroupMe said it was too tough but I don’t think it was that bad. The professors were very understanding, often extending deadlines when they saw we were having a hard time and even making some assignments extra credit.
Wrapping it up, this class was a favorite of mine at UCLA. It mixed fun with learning and was worth the 8-10 hours of weekly work outside lectures for an easy A.
Course was poorly taught and poorly managed. Projects were extremely difficult and cumbersome and extensions would be given morning of due date because people wouldn't be able to finish them. Midterm and final were fair though which was a redeeming factor.
Displaying all 3 reviews
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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67% recommend the textbook
Previous Grades
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