Large-Scale Machine Learning: The Problems, Algorithms, and Challenges
published: Jan. 19, 2010, recorded: December 2009, views: 1110
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
To seed discussion, I will attempt to organize research efforts in large-scale machine learning by looking at common computational problems across all of machine learning, and the challenges of creating efficient parallel algorithms for them. I'll begin by identifying four common types of computational bottlenecks that occur across all of machine learning, or prototype algorithmic problems: N-body problems, graph operations, linear algebra, and optimization. Within each category, I'll discuss what we can or cannot learn from the existing body of work in scientific computing, highlight a few of the most successful and recent specific serial algorithms that have been developed for concreteness, and discuss what makes them easy or hard to parallelize. I'll synthesize some of these observations to obtain a list of desiderata for parallel machine learning algorithms research and software toolkits.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !