A new crop of online courses has begun with the new year. To wrap my head around how GPU processing could be applied to Machine Learning and other Data Science areas I’ve enrolled in the University of Illinois’ Heterogeneous Parallel Programming via Coursera. This one started on January 6th. It began with Nvidia CUDA similar to how Udacity’s Intro to Parallel Programming is taught. The U-of-I class however plans to cover OpenCL and other interfaces after the groundwork is done with CUDA. I like this for avoiding potential vendor lock-in with CUDA only solutions.
On January 21st StatLearning: Statistical Learning from Stanford University begins. They intend to cover all of An Introduction to Statistical Learning in nine weeks! This will be a lot wider in topics than Andrew Ng’s Machine Learning class but cannot possibly go as deep in that time. A pdf of the book will be available at no cost. The R language will be used but without a syllabus its not clear how heavy of a programming load comes with this class. The course will be on an open source version of the edX platform directly from Stanford. It is not through edx.org.
Udacity starts the year rolling out their New Course Experience and a Data Science track. The topics there are certainly interesting but I’m already max’d out with the two above. I am also wait-and-see on the New Course Experience. A quick look at what was available late last year using the new Udacity approach left me thinking the free-to-learn track may be pretty light weight. More depth in the form of projects and tutoring comes with a per course monthly fee. No more all-you-can-eat (can-learn) for free from Udacity.