ITM 642 - Machine Learning for AI (3 )

GR

In this course, students will learn the principles and techniques of machine learning with a strong emphasis on practical applications. The curriculum covers supervised and unsupervised learning, including algorithms such as linear regression, decision trees, and support vector machines. Students will gain experience with Scikit-Learn and other machine learning libraries to build models for prediction, classification, and clustering tasks, and apply these techniques to real-world datasets.
PRE-REQ REQ or RECOMMENDED: Prerequisites: Python programming skill is a required prerequisite for this course. To satisfy this requirement, a student may take MDS 600 and MDS 630, or CSC 111 Programming Fundamentals, or transfer in an equivalent course showing python mastery. Documented non-academic python training will be considered on a case-by-case basis.

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