CST 238 - Machine Learning

This course provides students with an understanding of key concepts and practical applications in the field. It covers essential topics such as supervised and unsupervised learning, data preprocessing, and evaluation metrics. Through hands-on engagement with algorithms like linear regression and decision trees, students will gain practical experience. Students will explore intelligent software, algorithmic problem-solving, and the mathematical foundations of machine learning. Emphasizing real-world applications and ethical considerations, this course ensures practical insights for accessible and applicable learning.
Prerequisite(s): ENG 097, if required by DSP, CST 206, and MAT 144
2 lecture and 2 laboratory hours per week
3 credit hours