We selected this paper because it presents an easy to use algorithm that would be easy for you to code on python.
What to expect in one hour? We will spend 20 minutes explaining the essential parts of the paper and the next 40 minutes on python coding. We will also try to evaluate the results on some real data that can be found here: http://snap.stanford.edu/data/com-LiveJournal.html You are welcome to get yourself prepared by reading the paper and ask questions. If you are interested in deploying in large scale we can show you how to use out of the box graphlab algorithms.
A quick overview There are several online machine learning courses offered right now, Coursera, Udacity, Caltech, Stanford. This course is based on the “learning by example” principle. The students will be introduced in machine learning algorithms and data analysis techniques by actively working on a real dataset. Course style In this course we will not follow the traditional lecturing style with powerpoint style. It will be taught in lab sessions. We will have an one hour session every week. The instructor will present an algorithm along with a dataset. The students will use the language of their preference to code the algorithm and then apply it to the dataset.
Which day of the week will the course take place?Most likely every Friday 4pm to 6pm
When does the course begin? November 9th
Where do you meet every week?The course will take place in the LogicBlox office
Can people participate remotely?We are thinking seriously about that
Does the course include homeworks?Not in the way you mean it in college. You are not expected though to finish the lab assignments in the 2 hour sessions. You are expected to spend at least another 2 hours or more depending on how deeply you want to go into machine learning
Is there a limit for the number of people attending the class?Unfortunately we cannot host more than 15 people. We can probably accommodate about 20 more remote students
Will you provide the hardware for the course?You will have to bring your own laptop. We will try to get a cloud cluster for running the large scale jobs