CIS 205 — Machine Learning Essentials
This course introduces the fundamental concepts and techniques in machine learning (ML). It is designed for undergraduate students with a strong foundation in mathematics, programming, and basic data analysis. The course covers key algorithms used in supervised and unsupervised learning, including linear regression, decision trees, k-nearest neighbors, support vector machines, clustering, and neural networks. Students will also explore concepts such as overfitting, bias-variance tradeoff, cross-validation, and model evaluation. The course serves as a foundation for more advanced topics in machine learning and artificial intelligence. Writing assignments, as appropriate to the discipline, are part of the course.