University at Buffalo 3 credits Computer Science & Engineering
Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning. Pre-Requisite: CSE 250 and (EAS 305 or EE 305 or CIE 308 or CE 305 or MTH 411 or STA 301 LEC and STA 301 REC ) and MTH 309. Computer Science, Computer Engineering, or Bioinformatics majors only.
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