Most of the class will be devoted to supervised learning (in other words, learning in which a teacher provides the learner with the correct answers at training time). This is the most mature and widely used type of machine learning. We will cover the main supervised learning techniques, including decision trees, rules, instances, Bayesian techniques, neural networks, model ensembles, and support vector machines. We will also touch on learning theory with an emphasis on its practical uses. Finally, we will cover the two main classes of unsupervised learning methods: clustering and dimensionality reduction. Throughout the class there will be an emphasis not just on individual algorithms but on ideas that cut across them and tips for making them work.
In the class projects you will build your own implementations of machine learning algorithms and apply them to problems like spam filtering, clickstream mining, recommender systems, and computational biology. This will get you as close to becoming a machine learning expert as you can in ten weeks!