Description
This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. This course will also cover core foundational concepts underpinning and motivating modern machine learning and data mining approaches. This course will also cover some recent research developments.
Recommended prerequisites: algorithms, linear algebra, calculus, probability, and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156a or instructor’s permission)
Recommended prerequisites: algorithms, linear algebra, calculus, probability, and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156a or instructor’s permission)
General Information
Time and location
Tu/Th at 14:30-15:55 at 134 (Auditorium) BCK. Lectures will be recorded.
Gradescope entry code
DKB4KW
Office hour
Date/Time: Monday 4:30-5:30pm, Wednesday 7:00-8:00pm, Thursday 7:00-8:00pm.
Location: ANB 104.
Location: ANB 104.
Google calendar for lectures, office hours, and recitations
Staff Office Hours
Yisong Yue
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Natalie Bernat
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Stephen Ebaseh-Onofa
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Anwesha Das
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Dominic Phung
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Sanvi Pal
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Yingying Gong
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Daniel Khalil
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Siddhartha Ojha
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Ishita
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Shrujana Kunnam
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Anna Szczuka
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Christina Liu
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Madeline Egan
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Raaghav Malik
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Ryan Lin
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