1.3 Machine learning lecture 4

Instructor (Andrew Ng) :Okay, good morning. Just a few administrative announcements before we jump into today’s technical material. So let’s see, by later today, I’ll post on the course website a handout with the sort of guidelines and suggestions for choosing and proposing class projects.

So project proposals – so for the term project for this class due on Friday, the 19th of this month at noon – that’s about two weeks, two and a half weeks from now. If you haven’t yet formed teams or started thinking about project ideas, please do so.

And later today, you’ll find on the course website a handout with the guidelines and some of the details on how to send me your proposals and so on.

If you’re not sure whether an idea you have for a project may be a appropriate, or you’re sort of just fishing around for ideas or looking for ideas of projects to do, please, be strongly encouraged to come to my office hours on Friday mornings, or go to any of the TA’s office hours to tell us about your project ideas, and we can help brainstorm with you.

I also have a list of project ideas that I sort of collected from my colleagues and from various senior PhD students working with me or with other professors. And so if you want to hear about some of those ideas in topics like on natural [inaudible], computer vision, neuroscience, robotics, control. So [inaudible]ideas and a variety of topics at these, so if you’re having trouble coming up with your own project idea, come to my office hours or to TA’s office hours to ask us for suggestions, to brainstorm ideas with us.

Also, in the previous class I mentioned that we’ll invite you to become [inaudible] with 229, which I think is a fun and educational thing to do. So later today, I’ll also email everyone registered in this class with some of the logistical details about applying to be [inaudible]. So if you’d like to apply to be [inaudible], and I definitely encourage you to sort of consider doing so, please respond to that email, which you’ll get later today.

And finally, problem set one will also be posted online shortly, and will be due in two weeks time, so you can also get that online.

Oh, and if you would like to be [inaudible], please try to submit problem set one on time and not use late days for problem set one because usually select [inaudible]is based on problem set one solutions. Questions for any of that?

Okay, so welcome back. And what I want to do today is talk about new test methods [inaudible] for fitting models like logistic regression, and then we’ll talk about exponential family distributions and generalized linear models. It’s a very nice class of ideas that will tie together, the logistic regression and the ordinary V squares models that we’ll see. So hopefully I’ll get to that today.

So throughout the previous lecture and this lecture, we’re starting to use increasingly large amounts of material on probability. So if you’d like to see a refresher on sort of the foundations of probability – if you’re not sure if you quite had your prerequisites for this class in terms of a background in probability and statistics, then the discussion section taught this week by the TA’s will go over so they can review a probability.