Learning Algorithms. 1 credit, 1 contact hour

Catalog description

Prerequisites: MATH 111 , CS 100 with a grade C or better. An introduction to learning from data. The course covers the core ideas behind non-neural machine learning: what it means for an algorithm to generalize from examples, how predictions are made, and how performance is measured. Students work with decision trees, nearest-neighbor methods, and k-means clustering, developing hands-on fluency with computational tools and the standard evaluation and model-selection workflow. The course emphasizes intuition and correctness over mathematical depth.