CSCI 485 Machine Learning and Data Mining

This course covers the principles and practical applications of machine learning, a field that enables computers to learn patterns from data and make decisions or predictions. Students will explore key topics such as supervised and unsupervised learning, model evaluation, and advanced techniques like ensemble methods and neural networks. Practical assignments and projects will provide hands-on experience using machine learning libraries. By the end of the course, students will be equipped to build machine learning models, understand current research, and address real-world challenges across various domains.

Prerequisites: AI 425 Data Science with Generative AI and MATH 305 - Probability and Statistics.

Credits

3

Prerequisite

AI 425 Data Science with Generative AI and MATH 305 - Probability and Statistics. *AI 425 is a new course and will need to be added below.