Stanford's Machine Learning Course, ml-class.org

     After two months of octave and online videos, I finished Stanford's online course on machine learning.  I am very pleased with the course, and wish it could continue indefinitely.  The course consisted of online videos, content quizzes, and finally programming assignments in octave.  Topics covered include linear regression, neural networks, support vector machines, anomaly detection, and were presented in a way consistent with practical applications of these algorithms.  The programming exercises were taught using Octave, GNU's version of Matlab, and stressed vectorization for efficient computations. This enabling the students to implement and train a computational expensive neural network, and train it with backpropagation for optical character recognition.
     I learned a lot about linear algebra in the course, and developed an appreciation for its broad uses in numerical problems.  Learning more about linear algebra is a priority, and I plan to take a course on it during graduate school or sooner but for right now a handle on matrix multiplication and 3d coordinates will have to do.  Beyond basic linear algebra, the course was not very math heavy, requiring no proofs or formalism.  That being said, some of the vectorization required for the homework assignments was a little tricky, but nothing beyond  a little pen and paper work to figure out.
     If you are looking to learn more about machine learning in general, or would like to learn more about a specific algorithm covered in the course, I would recommend going to ml-class.org and watching some of the videos.  I am glad I took this course, and look forward to using the knowledge I gained in my research.