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The course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. It covers the mathematical methods and theoretical aspects, but will primarily focus on algorithmic and practical issues. Machine Learning and Pattern Recognition methods are at the core of many recent advances in “intelligent computing”. Current applications include machine perception (vision, audition, speech recognition), control (process control, robotics), data mining, time-series prediction (e.g. in finance), natural language processing, text mining and text classification, bio-informatics, neural modeling, computational models of biological processes, and many other areas.
This course can be useful to all students who would want to use or develop statistical modeling methods. This includes students in CS (AI, Vision, Graphics), Math (System Modeling), Neuroscience (Computational Neuroscience, Brain Imaging), Finance (Financial modeling and prediction), Psychology (Vision), Linguistics, Biology (Computational Biology, Genomics, Bio-informatics), and Medicine (Bio-Statistics, Epidemiology). The only formal pre-requisites are familiarity with computer programming and linear algebra, but the course relies heavily on such mathematical tools as probability and statistics, multi-variate calculus, and function optimization. The basic mathematical concepts will be introduced when needed, but students will be expected to assimilate a non-trivial amount of mathematical concepts in a fairly short time.
Although this is a graduate-level course, highly motivated undergraduates at the senior level with a good math background can take this class. A few juniors have even taken this class successfully in the past.
The topics studied in the course include:
By the end of the course, students will be able to not only understand and use the major machine learning methods, but also implement, apply and analyze them.