Pattern Recognition and Machine Learning

(Same as Statistics M231A.) Lecture, three hours; discussion, one hour. Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting. S/U or letter grading.

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Section List

  • LEC 1

    Closed

    MW 5pm-6:15pm

    Renee and David Kaplan Hall A65

Course

Instructor
Alyson Fletcher
Previously taught
23F

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