Build the future with Artificial Intelligence

This course offers a survey of current database approaches and systems, as well as the principles of design and use of these systems. It covers query language design and implementation constraints, as well as applications of large databases. Course topics include a survey of file structures and access techniques, as well as the use of a relational database management system to implement a database design project.

This course includes topics on algorithm design techniques; methods for evaluating algorithm efficiency; data structure specification and implementation; and applications to fundamental computational problems in sorting and selection, graphs and networks, scheduling and combinatorial optimization, computational geometry, and arithmetic and matrix computation. It also introduces students to parallel algorithms and to computational complexity and a survey of NP-complete problems.

This course covers theoretical and practical aspects of operating systems, including an overview of system software, time-sharing and multiprogramming operating systems. Students also learn about network operating systems and the internet, virtual memory management, interprocess communication and synchronization, file organization, and case studies.

This course introduces the state of the art in uniprocessor computer architecture, with a focus on the quantitative analysis and cost-performance trade-offs in instruction set, pipeline and memory design. Topics include quantitative analysis of performance and hardware costs, instruction set design, pipeline, delayed branch, memory organization, and advanced instruction-level parallelism.

This course introduces students to basic principles and current research topics in artificial intelligence. It covers the formal representation of real-world problems; the search of problem spaces for solutions; and the deduction of knowledge in terms of predicate logic, nonmonotonic reasoning and fuzzy sets. Students also learn how to apply these methods to important areas of artificial intelligence, including expert systems, planning, language understanding, machine learning, neural networks, computer vision and robotics.

This course introduces the processes of learning from data, providing an overview of a number of machine learning techniques used in analytics, including pre-processing, visualization, classification and regression. It covers classic and contemporary learning techniques, with emphasis on artificial neural networks and deep learning methods. Students also complete hands-on projects using state-of-the art tools. Class examples and assignments come from the programming language Python.

There is an associated application fee.

A bachelor’s degree in one of the quantitative sciences, mathematics or computer science or in one of the engineering disciplines, a minimum GPA of 3.0 on a 4.0 scale in your undergraduate studies, and a minimum of one year of college-level calculus are required. Please note that applicants with undergraduate degrees in disciplines other than computer science may be admitted to the program but may be required to take articulation coursework and/or satisfy the competency requirement.

A minimum of two years of industry experience or submission of official GRE general graduate school admission test scores.