In-depth introduction to Artificial Intelligence concentrating on aspects of intelligent agent construction. Topics include: situated agents,advanced search and problem-solving techniques, principles of knowledge representation and reasoning, reasoning under uncertainty, perception and action, automated planning, and learning.

This course provides an in-depth examination of the fundamental principles of information assurance: authentication, integrity, confidentiality of distributed systems, network security, malware, privacy, intrusion detection, intellectual property, and protection. Prerequisite: CMPSCI 460 (Introduction to Computer and Network Security), or 466 (Applied Cryptography).

Basic and advanced techniques for text-based information systems, including retrieval models, indexing and text representation, browsing and query formulation, data-intensive computing approaches, evaluation, and issues surrounding implementation. The course will include a substantial project such as implementation of major elements of search engines and applications.

Covers topics such as the relational model, query languages, database design; storage and indexing, query processing and optimization, transaction management; XML data management; information integration; data stream systems; database security.

The current best testing and analysis practices; emerging new approaches, going beyond the testing of programs to include the testing and analysis of all software artifacts across the lifecycle. Prerequisite: CMPSCI 320.

The design and analysis of efficient algorithms for important computational problems. Paradigms for algorithm design including Divide and Conquer, Greedy Algorithms, Dynamic Programming; and, the use of Randomness and Parallelism in algorithms. Algorithms for Sorting and Searching, Graph Algorithms, Approximation Algorithms for NP Complete Problems, and others. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of CMPSCI 311.

Basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional (Pascal-like) programming languages to produce assembly or object code for typical machines. Involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language within a provided skeleton.

Basic problems in the translation of programming languages focusing on theory and common implementation techniques for compiling traditional (Pascal-like) programming languages to produce assembly or object code for typical machines. Involves a substantial laboratory project in which the student constructs a working compiler for a considerable subset of a realistic programming language within a provided skeleton.

Not available at this time

We will introduce neural networks and modeling of brain functions: the inspiration, engineering applications, theoretical analysis, as well as psychological and biological modeling. We will review different neural networks within the two groups: Feed forward networks, applications, and approximation theorem; Recurrent neural networks and computational power; We will also focus on supervised and unsupervised learning with applications in clustering; representability; and Models of memory, diseases, and healthy perception. This course counts as a CS Elective toward the CMPSCI major.