Machine Learning studies representations and algorithms that allow machines to improve their performance on a task from experience. 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! This course is a broad overview of existing methods for machine learning and an introduction to adaptive systems in general. Emphasis is given to practical aspects of machine learning and data mining. 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: Thursday 3:15-4:15pm, A.V.Williams 3251
Joseph Thomas Bergman
Office hours: Monday 3:00 - 4:00pm, A.V.Williams 4103 (mainly section 0201)
Office hours: Tuesday 9:00 - 10:00am, A.V.Williams 4103 (mainly section 0201)
Office hours: Tuesday 3:30 - 4:30pm, A.V.Williams 4101 (mainly section 0101)
Office hours: Wednesday 10:00 - 11:00am, A.V.Williams 4101 (mainly section 0101)
Office hours: Wednesday 3:00 - 4:00pm, A.V.Williams 4103 (mainly section 0201)
If you're a registered student, send a private post to instructors on Piazza. If not, send an email including "422" in the title (not recommended).