Machine learning lecture 1  (Page 8/17)

So my friend was very excited. He said, "Wow. That's great. I'm glad to hear this machine learning stuff was actually useful. So what was it that you learned? Was it logistic regression? Was it the PCA? Was it the data networks? What was it that you learned that was so helpful?" And the student said, "Oh, it was the MATLAB."

So for those of you that don't know MATLAB yet, I hope you do learn it. It's not hard, and we'll actually have a short MATLAB tutorial in one of the discussion sections for those of you that don't know it.

Okay. The very last piece of logistical thing is the discussion sections. So discussion sections will be taught by the TAs, and attendance at discussion sections is optional, although they'll also be recorded and televised. And we'll use the discussion sections mainly for two things. For the next two or three weeks, we'll use the discussion sections to go over the prerequisites to this class or if some of you haven't seen probability or statistics for a while or maybe algebra, we'll go over those in the discussion sections as a refresher for those of you that want one.

Later in this quarter, we'll also use the discussion sections to go over extensions for the material that I'm teaching in the main lectures. So machine learning is a huge field, and there are a few extensions that we really want to teach but didn't have time in the main lectures for.

So later this quarter, we'll use the discussion sections to talk about things like convex optimization, to talk a little bit about hidden Markov models, which is a type of machine learning algorithm for modeling time series and a few other things, so extensions to the materials that I'll be covering in the main lectures. And attendance at the discussion sections is optional, okay?

So that was all I had from logistics. Before we move on to start talking a bit about machine learning, let me check what questions you have. Yeah?

Student : [Inaudible] R or something like that?

Instructor (Andrew Ng) : Oh, yeah, let's see, right. So our policy has been that you're welcome to use R, but I would strongly advise against it, mainly because in the last problem set, we actually supply some code that will run in Octave but that would be somewhat painful for you to translate into R yourself. So for your other assignments, if you wanna submit a solution in R, that's fine. But I think MATLAB is actually totally worth learning. I know R and MATLAB, and I personally end up using MATLAB quite a bit more often for various reasons. Yeah?

Student : For the [inaudible] project [inaudible]?

Instructor (Andrew Ng) : So for the term project, you're welcome to do it in smaller groups of three, or you're welcome to do it by yourself or in groups of two. Grading is the same regardless of the group size, so with a larger group, you probably — I recommend trying to form a team, but it's actually totally fine to do it in a smaller group if you want.

Student : [Inaudible] what language [inaudible]?

Instructor (Andrew Ng): So let's see. There is no C programming in this class other than any that you may choose to do yourself in your project. So all the homeworks can be done in MATLAB or Octave, and let's see. And I guess the program prerequisites is more the ability to understand big?O notation and knowledge of what a data structure, like a linked list or a queue or binary treatments, more so than your knowledge of C or Java specifically. Yeah?