Machine learning is all about finding patterns in data to get computers to solve complex problems. Instead of explicitly programming computers to perform a task, machine learning lets us program the computer to learn from examples and improve over time without human intervention. This requires addressing a difficult question: how to generalize beyond the examples that have been provided at "training time" to new examples that you see at "test time". This course will show you how this generalization process can be formalized and implemented. We'll look at it from lots of different perspectives, illustrating the key concepts in the field.
It's an exciting time to study machine learning! The techniques we will cover are broadly applicable, and have led to significant advances in many fields, including stock trading, robotics, machine translation, computer vision, medicine and many more. In addition, once you understand the basics of machine learning technology, and the close connection betwen theory and practice, it's a very open field, where lots of progress can be made quickly.
Office hours: Thursday 3:15-4:15pm, A.V.Williams 3157
Office hours: Monday 11:45am-12:45pm, A.V.Williams 4101/4103
If you're a registered student, send a private post to instructors on Piazza. If not, send an email including "422" in the title.