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CISC 3440 Machine Learning

3 hours; 3 credits

An introduction to machine learning for students with some mathematical maturity. Topics include: machine learning in relation to artificial intelligence, data sources and characteristics, linear and non-linear regression, machine learning concepts like the bias-variance tradeoff, linear and non-linear classification, hidden Markov models and the expectation-maximization algorithm, unsupervised learning, and deep learning. Examples will be drawn from several domains including natural language processing.

Prerequisite: Computer and Information Science 3130 or 3225; MATH 2501 or 3501 or Computer and Information Science 2210.


The City University reserves the right, because of changing conditions, to make modifications of any nature in academic programs and requirements of the university and its constituent colleges without advanced notice. Students are advised to consult regularly with college and department counselors concerning their programs of study.

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