Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
