Take a slight digression, talk about Gaussians, and I'll briefly discuss generative versus discriminative learning algorithms, and then hopefully wrap up today's lecture with a discussion of Naive Bayes and the Laplace Smoothing.

We'll then talk about a probabilistic interpretation of linear regression and use that to move onto our first classification algorithm, which is logistic regression take a brief digression to tell you about something called the perceptron algorithm, which is something we'll come back to, again, later this quarter and time allowing I hope to get to Newton's method, which is an algorithm for fitting logistic regression models.

I started to discuss in the previous lecture and talk about a couple of different event models in Naive Bayes, and then I'll take a brief digression to talk about neural networks, which is something that I actually won't spend a lot of time on, and then I want to start to talk about support vector machines, and support vector machines is the learning algorithms, the supervised learning algorithm that many people consider the most effective, off the shelf supervised learning algorithm.