Pattern Recognition and Machine Learning
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|Location:||Barus & Holley 159|
|Meeting Time:||K: TTh 2:30-3:50|
|Offered This Year?||Yes|
|When Offered?||Every Year|
This course covers fundamental topics in pattern recognition and machine learning. We will consider applications in computer vision, signal processing, speech recognition and information retrieval. Topics include: decision theory, parametric and non-parametric learning, dimensionality reduction, graphical models, exact and approximate inference, semi-supervised learning, generalization bounds and support vector machines. Prerequisites: basic probability, linear algebra, calculus and some programming experience.