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.
Also running on Coursera right now is Roger Peng’s Computing for Data Analysis, a nice short introduction to R. I highly recommended this before taking Jeff Leek’s Data Analysis which uses R for programming assignments. These are from Johns Hopkins University and were offered twice during 2013. Jeff’s class is not presently on the Coursera schedule but I expect it will show up shortly after Roger’s completes. These two courses were the starting point for many of the Boulder Data Detectives study group members on their journey in to the world of data analysis, big data, and machine learning.
And furthermore …
It looks like a moderate amount of smarts in the image processing area could be quite helpful in machine learning classifier feature generation.
Both Fundamentals of Digital Image and Video Processing from Northwestern and Image and video processing: From Mars to Hollywood with a stop at the hospital from Duke look useful.
Also Digital Signal Processing from École Polytechnique Fédérale de Lausanne appears to be the place to go for a deep dive in to the theory and algorithms behind much of this area.