An interdisciplinary class contrasting machine learning with learning in biological systems, particularly children. Plausible models of learning. Childhood acquisition of knowledge. Occam’s Razor, the Bayesian Occam’s Razor, and PAC Learning. Frequentist vs. Bayesian Inference. Kolmogorov Complexity. Hypothesis formation and evaluation. Learning from the real-world without labels. Innate vs. Acquired Knowledge. Acquisition of Language. Human rationality and Prospect Theory. Prereq: CS 540 or consent of instructor.