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Detailed Course Information

 

Spring 2020
Sep 17, 2019
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CSCI 1420 - Machine Learning
We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian and maximum likelihood parameter estimation, regularization and sparsity-promoting priors, kernel methods, the expectation maximization algorithm, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples from vision, language, bioinformatics, and information retrieval. Comfort with basic Multivariable Calculus is recommended.
1.000 Credit hours
1.000 Lecture hours

Levels: Graduate, Undergraduate
Schedule Types: Primary Meeting

Computer Science Department

Prerequisites:
(Undergraduate level CSCI 0160 Minimum Grade of S or Undergraduate level CSCI 0180 Minimum Grade of S or Undergraduate level CSCI 0190 Minimum Grade of S) and (Undergraduate level MATH 0100 Minimum Grade of S or Undergraduate level MATH 0170 Minimum Grade of S or Undergraduate level MATH 0180 Minimum Grade of S or Undergraduate level MATH 0190 Minimum Grade of S or Undergraduate level MATH 0200 Minimum Grade of S or Undergraduate level MATH 0350 Minimum Grade of S) and (Undergraduate level CSCI 1450 Minimum Grade of S or Undergraduate level APMA 1650 Minimum Grade of S or Undergraduate level APMA 1655 Minimum Grade of S) and (Undergraduate level CSCI 0530 Minimum Grade of S or Undergraduate level MATH 0520 Minimum Grade of S or Undergraduate level MATH 0540 Minimum Grade of S)

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