Probabilistic Approaches to Perception

Covers perception as inference, with attention to (a) modern Bayesian approaches to perception (and causal learning), (b) historical approaches such as Brunswik's probabilistic functionalism, and (c) modern machine learning theory. Assumes the ability to understand and learn mathematics at a calculus level or better, but not prior exposure to Bayesian statistics per se.  We will do some exercises requiring computer simulation so programming ability is essential.