Dave Blei writes:
This course is cross listed in Computer Science and Statistics at Columbia University.
It is a PhD level course about applied probabilistic modeling. Loosely, it will be similar to this course.
Students should have some background in probability, college-level mathematics (calculus, linear algebra), and be comfortable with computer programming.
The course is open to PhD students in CS, EE and Statistics. However, it is appropriate for quantitatively-minded PhD students across departments. Please contact me [Blei] if you are a PhD student who is interested, but cannot register.
Research in probabilistic graphical models has forged connections between signal processing, statistics, machine learning, coding theory, computational biology, natural language processing, computer vision, and many other fields. In this course we will study the basics and the state of the art, with an eye on applications. By the end of the course, students will know how to develop their own models, compute with those models on massive data, and interpret and use the results of their computations to solve real-world problems.
Looks good to me!