CSCI 297 - Topics in Computer Science
Prerequisite: CSCI 112. Readings and conferences for a student or students on topics agreed upon by the directing staff. May be repeated for degree credit if the topics are different. A maximum of six credits may be used toward the major requirements. Offered when interest is expressed and departmental resources permit.
Fall 2020, CSCI 297A-01: Topic: An Exploration of Canonical Machine-Learning Methods (3). Prerequisite: CSCI 112. Analyzing and implementing traditional machine learning models to understand the relationships amongst the features of that data. Students learn the process of feature discovery and engineering, in conjunction with statistical algorithms, to “learn” from the data. These algorithms allow for automatically applying complex mathematical calculations over large datasets. Learning from data affords us the ability to identify patterns and make informed decisions without human intervention. We explore regression algorithms, supervised algorithms, naïve Bayes classifiers, unsupervised algorithms, dimensionality reduction, and clustering algorithms. C. Watson. Staff.
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