-
Hello everyone, and welcome to the class on probabilistic graphical models.
-
My name is Daphne Koller 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 in an upcoming video
-
but also throughout the entire class.
-
In this video, I’d like to tell you a little bit about the format of this class.
-
The course is going to be offered over ten weeks worth of material
-
plus a final examination at the end.
-
The content is going to be conveyed via a set of videos,
-
augmented with quizzes to reinforce understanding.
-
In addition, there is going to be a weekly problem set
-
where the problem sets altogether are going to be worth 25% of the score
-
for a total of the nine problem sets for the nine weeks worth of content.
-
The problem sets are designed to allow for multiple submissions,
-
so that each version of the problem set is going to be a little bit different
-
so that you can resubmit the same problem set [a] couple of times
-
to make sure that you really mastered the material.
-
In addition, there’s going to be a weekly programming assignment,
-
and those programming assignments were selected
-
to reinforce specific concepts that we’re studying in the course,
-
but at the same time to reveal the range of applications
-
to which the framework of probabilistic graphical models can be successfully applied.
-
So we’re going to have, for example,
-
a problem set on how you use probabilistic graphical models
-
to understand the inheritance of genetically inherited diseases.
-
We’re going to have one that shows
-
how you can look at a set of handwritten characters
-
and read what’s written there.
-
And we’re going to have one that allows you
-
to look at a stream of output from a Kinect sensor
-
that gives you both video and range data
-
and recognize human activities.
-
These nine programming assignments are each going to be worth 7% of the score
-
for a total of 63%,
-
which gives us 12% left for the final exam.
-
What background do you need for this class?
-
Well, it’s going to be really hard to do this class,
-
without some understanding of basic probability theory.
-
This doesn’t have to be very advanced stuff.
-
We’re talking about things like independence and Bayes' rule
-
And just basics of discrete distributions.
-
And we also have a few introductory modules
-
to refresh your memory about these basic concepts.
-
The programming assignments will require
-
that you’ve had some experience programming before
-
because this is not a 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, perhaps, in machine learning,
-
maybe some simple optimization like gradient descent,
-
nothing very sophisticated.
-
And it would be helpful to have some experience programming in 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 when they’re taking a 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 sets and 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 that future generations of students can do
-
the problems sets and the programming assignments independently as well.
-
A second issue to keep in mind is that of time management.
-
This is a graduate-level Stanford class
-
and it’s considered a difficult one even at Stanford.
-
A typical Stanford student can easily spend
-
ten to fifteen hours 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 a submission deadline comes around.
-
We’ve built in a little slack into the submission deadline,
-
so 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 week’s problem set.
-
So we advise that you don’t just keep
-
a backlog of assignments throughout the course,
-
because it will all end up coming back to bite 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 a much better understanding of the material
-
and will make the course considerably more fun as well.
-
So, to summarize,
-
through 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.
