A broad introduction to machine learning and statistical pattern recognition. Unsupervised and supervised learning algorithms including dimensionality reduction (PCA and variants), clustering (simple clustering, agglomerative and non-agglomerative), probabilistic models, neural networks, and support vector machines. Hours: 4. Prerequisite: ECON 2227 or EVST 2205 or MATH 2227 or MATH 3343 or POL 2231 or PSYC 2227 or SOC 3395.
- Teacher: Katrin Schenk