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Course Description: Machine Learning is the study of how to build computer systems that learn from experience. This CS494/594 course on Projects in Machine Learning will explain how to build systems that learn and adapt using real-world applications (such as robotics and brain wave signal understanding). Some of the topics to be covered include reinforcement learning, neural networks, genetic algorithms and genetic programming, parametric learning (density estimation), clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems. Most projects will include written reports. No exams will be given.
Prerequisites: Familiarity with basic concepts of computer science (algorithms, data structures, and complexity), mathematical maturity in discrete math (CS311), matrix math (Math 251), probability and statistics (Math 323), and ability to program algorithms in a language of your choice.
Instructor: Prof. Lynne E. Parker
TA: Michael Bailey