Hello everyone and welcome to the class on probabilistic graphical models. My name is Daphne Collier and I'm a professor at Stanford University. We here at Stanford are really excited to be able to offer this graduate level Stanford class to anyone anywhere around the world for free. So what are probabilistic graphical models? Well it's a bit complicated to explain and we're going to talk about that and up coming video but also for out entire class. What background do you need for this class? Well its going to be really hard to do this class without some understanding of basic probability theory. This also have to be very advance stuff we're talking about things like independence and days role and just basics of discreet distributions, and we also have a few introductory modules to refresh your memory about these basic concepts. The programming assignments will require that you have some experience programming before because this is not programming class. We don't teach you how to program. And because this class merges ideas from both probability theory and computer science, it's really important you have some background in algorithms and data structures. Recommended but not strictly necessary and we certainly don't require it. And we give you the background as we go, is a little bit of experience for how some machine learning, may be some simple optimization like gradient descent, nothing very sophisticated. And it will be helpful to have some experience Promium and Matlab or Octave although here also, we have some introductory modules that help you learn this programming language if you haven't played around with it before. A few other issues that are worth noting, this class has an honor code. This is the norm also for our local Stanford students who are taking Stanford class. The honor code here says that you're allowed to discuss the material, in fact even encouraged to discuss the material with your fellow classmates. You can even ask clarifying questions about the problems that's in the programming assignments but what you turn in has to be your own work. Furthermore we request that you do not post either the programming assignments or their solutions anywhere on the web so, the future generations of students can do the problems that's the assignments independently as well. A second issue to keep in mind is Time Management. This is a graduate level stand for class and it's consider the difficult one even in Stanford. A typical Stanford student can easy spend 10 to 15 hrs a week on this class. And so we would suggest that you budget at least that amount of time for your own efforts on this class if you don't want to find yourself running out of time when the submission deadline comes around. We've built in a little bit of slack into the submission deadline so that if you don't manage to submit by the original deadline you have a week's worth of grace period. But then that obviously starts to impinge on the next weeks problem set, so we advise that you don't just keep a backlog of assignments throughout the course because it'll all end up coming back to bit you in the end. Finally, part of the experience of this class is interacting with your fellow students. So, for that purpose, we have the discussion forum, which has proven, in other classes, to be an invaluable resource for interacting with other students, asking questions, and obtaining a deeper understanding of the material. We're also encouraging you to form study groups. These can be physical study groups, with people in the same geographical region, or online study groups where you can just discuss the material with each other. We believe that doing this will give you much better understanding of the material and will make the course considerably more fun, as well. So to summarize for all these different pieces of the content and the exercises we think that you'll learn fundamental methods in this area of probabilistic graphical models. You'll also get to see and play around with a range of real world applications for which these methods have been applied and hopefully you will leave this class with an understanding of how to take these ideas and use them in your own work in problems that you care about. We look forward to seeing you in this class.
