Study of computational principles and techniques that enable software systems to improve their performance by learning from data. Focus on fundamental algorithms, mathematical models and programming techniques used in Machine Learning. Topics include: different learning settings (such as supervised, unsupervised and reinforcement learning), various learning algorithms (such as decision trees, neural networks, k-NN, boosting, SVM, k-means) and crosscutting issues of generalization, data representation, feature selection, model fitting and optimization.

The course covers basic techniques of artificial intelligence. The topics in this course are: search and game-playing, logic systems and automated reasoning, knowledge representation, intelligent agents, planning, reasoning under uncertainty, and declarative programming languages. The course covers both theory and practice, including programming assignments that utilize concepts covered in lectures.

This course focuses on advanced computer architectures and low-level system software. Topics include RISC architectures, vector and multiprocessor architec- tures, multiprocessor memory architectures, and multiprocessor interconnection networks. Peripheral devices such as disk arrays, NICs, and video/audio devices are covered. Topics also include device drivers, interrupt processing, advanced assembly language programming techniques, assemblers, linkers, and loaders.

Study of new topics and emerging research and methods in computer science. A review and extension of selected topics in the current literature. When the course is offered, a specific title with specific credits, the number of hours in lecture/discussion and laboratory/practicum will be announced. Lecture/discussion, one-three hours; laboratory/practicum, zero-three hours per week. May be repeated up to the discretion of the department under different subtitles.

Study of new topics and emerging research and methods in computer science. A review and extension of selected topics in the current literature. When the course is offered, a specific title with specific credits, the number of hours in lecture/discussion and laboratory/practicum will be announced. Lecture/discussion, one-three hours; laboratory/practicum, zero-three hours per week. May be repeated up to the discretion of the department under different subtitles.

Study of new topics and emerging research and methods in computer science. A review and extension of selected topics in the current literature. When the course is offered, a specific title with specific credits, the number of hours in lecture/discussion and laboratory/practicum will be announced. Lecture/discussion, one-three hours; laboratory/practicum, zero-three hours per week. May be repeated up to the discretion of the department under different subtitles.

Current approaches -- practice and technologies -- for developing reliable software: specifications, testing, and verification. Individual and team assignments focused on applying these approaches to software systems. A significant communication and composition component related to specifying, designing, presenting, and documenting software systems.

Projects to design and implement complex systems of current interest to computer scientists. Students will work in small groups. This course is a Graduation Composition and Communication Requirement (GCCR) course in certain programs, and hence is not likely to be eligible for automatic transfer credit to UK.

Projects to design and implement complex systems of current interest to computer scientists. Students will work in small groups. This course is a Graduation Composition and Communication Requirement (GCCR) course in certain programs, and hence is not likely to be eligible for automatic transfer credit to UK.

The design and analysis of efficient algorithms and data structures for problems in sorting, searching, graph theory, combinatorial optimization, computational geometry, and algebraic computation. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy method, and randomization, approximation algorithms.