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Course Overview Provides a fundamental understanding of the problems in natural language understanding by computers, and the theory and practice of current computational linguistic systems. Of interest to students of artificial intelligence, algorithms, and the computational processes of comprehension and understanding.
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The quest to understand the most complex object in the known universe has been a long and fruitful one. These days we know a good deal about how the human brain works - how our senses translate into electrical signals, how different parts of the brain process these signals, how memories form and how muscles are controlled. We know which brain regions are active when we listen to speech, look at paintings or barter over money. We are even starting to understand the deeper neural processes behind learning and decision-making.
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# Balanced treatment of the simplex method and interior-point methods. # Efficient source code (in C) for all the algorithms presented in the text. # Thorough discussion of several interior-point methods including primal-dual path-following, affine-scaling, and homogeneous self dual methods. # Extensive coverage of applications including traditional topics such as network flows and game theory as well as less familiar ones such as structural optimization, L^1 regression, and the Markowitz portfolio optimization model. # Over 200 class-tested exercises. # A dynamically expanding collection of exercises.
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