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Detailed Course Information

 

Spring 2020
Feb 20, 2020
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CSCI 1420 - Machine Learning
How can artificial systems learn from examples and discover information buried in data? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised learning. Specific topics include empirical risk minimization, probably approximately correct learning, kernel methods, neural networks, maximum likelihood estimation, the expectation maximization algorithm, and principal component analysis. This course also aims to expose students to relevant ethical and societal considerations related to machine learning that may arise in practice.
Please contact the instructor for information about the waitlist.
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 or AP Calculus BC 4 or IB HL Mathematics 5) 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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