|Time/place||TTh 12:30pm-1:45pm 1320 DCL / Catalog|
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The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. The main body of the course will review several supervised and (semi/un)supervised learning approaches. These include methods for learning linear representations, Bayesian / Probabilistic methods, decision-tree methods, kernel based methods and neural networks, as well as clustering and dimensionality reduction techniques. We will also discuss how to model machine learning problems and discuss some open problems.
Exam will be in class exam with 75 minutes. Please find information on our upcoming exams in the corresponding section of the class calendar.
Please see the class calendar for homework deadlines.
|name and role||contact (illinois.edu)||office hours||location|
|Sanmi Koyejo | Instructor||sanmi||Wednesday 2pm - 3pm or by appointment||3314 SC|
|Chase Duncan | Course Coordinator||cddunca2||by appointment||3333 SC|
|Yeech Zhu | TA||yzhu44||Thursday 6pm - 8pm||Whiteboard outside of 3303 SC|
|Daniel Calzada | TA||dcalzad2||Monday 7pm - 9pm||Whiteboard outside of 3333 SC|
|Ping-Ko Chiu | TA||pchiu5||Tuesday 4pm - 6pm||Whiteboard outside of 3333 SC|
|Gaurush Hiranandani | TA||gaurush2||Friday 11am - 1pm||Whiteboard outside of 3333 SC|
We will be using Python with the libraries numpy, scipy and matplotlib for assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.