2009 Machine Learning - Stanford University. MCMC practical. This page contains the practical that went with my tutorial lectures on Markov chain Monte Carlo (MCMC) at the Machine Learning Summer School (MLSS) in Cambridge, 2009.
You can download a .zip archive of all of the files on this webpage. As noted below, I suggest that you do not look at inference.m until you have seriously attempted to get the samplers running on the parameter posterior yourself. Electronic copy of the handout. MIT Visualization. 2011 Machine Learning - Stanford University. Machine learning is the science of getting computers to act without being explicitly programmed.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.