This course introduces students to the mathematical theory and practical application of modern machine learning. Topics include clustering, regression, classification, neural networks, reinforcement learning, and other AI topics. Students will complete a significant machine learning project that requires them to process data, minimize bias, analyze trade-offs, evaluate their model, and communicate their conclusions. They will also consider how fundamental beliefs influence perspectives on the limits and ethics of this technology. Prerequisites: Junior or Senior standing; C- or better in CSC 212, CSC 314, and MTH 265.