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Detailed Course Information


Fall 2019 Semester
Jul 28, 2021
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DS 5220 - Supervised Machine Learning and Learning Theory
Introduces supervised machine learning, which is the study and design of algorithms that enable computers/machines to learn from experience or data, given examples of data with a known outcome of interest. Offers a broad view of models and algorithms for supervised decision making. Discusses the methodological foundations behind the models and the algorithms, as well as issues of practical implementation and use, and techniques for assessing the performance. Includes a term project involving programming and/or work with real-life data sets. Requires profiency in a programming language such as Python, R, or MATLAB.
4.000 Credit hours
4.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Data Science Department

Course Attributes:
Graduate CCIS Data Sci Cert

Must be enrolled in one of the following Programs:     
      MS Data Science
Must be enrolled in one of the following Levels:     

Graduate level CS 5800 Minimum Grade of C or Graduate level EECE 7205 Minimum Grade of C

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