Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore formal models of learnability in this 48-minute machine learning lecture focusing on batch learning and computational learning theory. Delve into the mistake bound model before examining what formal models of learnability provide and understanding the goals of batch learning. Learn about learning conjunctions, random source training examples, nature-provided labels, and different approaches to evaluating learning algorithms. Understand key concepts including distribution over instance space, PAC Learning intuition, hypothesis error, empirical error, and the distinctions between online and batch learning methodologies.