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This course provides a rigorous introduction to the design and analysis of algorithms. We discuss problems (e.g. sorting, traveling salesman problem), classic algorithm design strategies (e.g. divide-and-conquer, greedy approaches), and data structures (e.g., hash tables, Dijkstra algorithm). We also analyze algorithm complexity throughout, and touch on issues of "NP-Completeness".
This course provides a review for the design and analysis techniques of algorithms. We discuss advanced algorithms with respect to complex problems. We also analyze complexity throughout, and discuss NP problems, reducibility, and satisfiability.
This course is concerned with understanding the fundamentals of digital image perception, representation, processing, and compression.
This course covers pattern recognition essentials with topics that are suitable for undergraduate level in computer science and most engineering specialties. It covers a variety of related topics such as: pattern recognition systems, preprocessing and feature extraction, theories of statistical pattern recognition, and other topics.
This course provides a rigorous introduction to that subset of the Project Management Body of Knowledge that is generally recognized as good practices in accordance with the standards of the Project Management Institute. The course also provides a common lexicon for discussing, writing, and applying project management.
This course provides description for database systems including, creation, data manipulation, indexing, optimization and concurrency control.
This course provides an introduction to basic mathematical structures that are applicable to Computer Science.