MATH 156
Machine Learning
Description: 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.
Units: 4.0
Units: 4.0
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Most Helpful Review
Summer 2021 - Just take him and you won't regret it! Easily one of the best math professors I've taken at UCLA. Also, keep in mind that he taught (and created) this course back in 2015(6?), so he is very clear and knowledgeable with the materials of the class. This class is quite statistics/probability heavy, so it's best if you ace your math 170E/A or stats 100A before taking it as your understandings of the lecture really depends on it.
Summer 2021 - Just take him and you won't regret it! Easily one of the best math professors I've taken at UCLA. Also, keep in mind that he taught (and created) this course back in 2015(6?), so he is very clear and knowledgeable with the materials of the class. This class is quite statistics/probability heavy, so it's best if you ace your math 170E/A or stats 100A before taking it as your understandings of the lecture really depends on it.
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