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Syllabus | CS 189/289A
CS 189/289A (Introduction to Machine Learning) covers: Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression a...
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Resources | CS 189/289A
Tips These tips have been collected through the years from professors, past and present. You can also check out the Learning How To Learn Coursera course for other general tips. Don’t fall behind I...
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Past Exams | CS 189/289A
Past Exams Below are past exams from different iterations of the course. Note that the fall iterations tend to be similar to each other, and the spring iterations tend to be similar to each other. Th...
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Calendar | CS 189/289A
Calendar Below are the timings for lectures, office hours, homework party, and discussion sections. This page will be updated periodically as times change. Lectures Dwinelle 155 on Tuesdays and Th...
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CS 189
Homeworks All homeworks are partially graded and it is highly-recommended that you do them. Your lowest homework score will be dropped, but this drop should be reserved for emergencies. Here is the se...
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Home | CS 189/289A
Introduction to Machine Learning University of California, Berkeley, Fall 2023 Welcome to CS 189/289A! This class covers theoretical foundations, algorithms, methodologies, and applications for machi...