Lecture, three hours; discussion, one hour. Requisites: courses 115A, 164, 170A or 170E or Statistics 100A, and Computer Science 31 or Program in Computing 10A. Strongly recommended requisite: Program in Computing 16A or Statistics 21. Introductory course on mathematical models for pattern recognition and machine learning. Topics include parametric and nonparametric probability distributions, curse of dimensionality, correlation analysis and dimensionality reduction, and concepts of decision theory. Advanced machine learning and pattern recognition problems, including data classification and clustering, regression, kernel methods, artificial neural networks, hidden Markov models, and Markov random fields. Projects in MATLAB to be part of final project presented in class. P/NP or letter grading.

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Enrollment Progress

Jul 10, 11 PM PDT
LEC 1: 40/40 seats taken (Full)
Priority PassFirst PassSecond Pass0 days3 days7 days11 days15 days17 days21 days25 days0204060

Section List

  • LEC 1

    Full

    MWF 10am-10:50am

    Boelter Hall 5436

Course

Instructor
Halyun Jeong
Previously taught
21F

Previous Grades

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Textbooks

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