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Detailed Course Information

 

Fall 2016
Sep 23, 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. Prerequisites: CSCI 0040 or 0150 or 0180 or 0190; and CSCI 0450 or CSCI 1450 or APMA 1650 or MATH 1610; and CSCI 0530 or MATH 0520 or 0540; or instructor permission.
1.000 Credit hours
1.000 Lecture hours

Levels: Graduate, Undergraduate
Schedule Types: Primary Meeting

Computer Science Department

Prerequisites:
(Undergraduate level CSCI 0040 Minimum Grade of S or Undergraduate level CSCI 0150 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 CSCI 0450 Minimum Grade of S or Undergraduate level APMA 1650 Minimum Grade of S or Undergraduate level CSCI 1450 Minimum Grade of S or Undergraduate level MATH 1610 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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