Introduction to formal language theory. Topics include finite state languages, context-free languages, the relationship between language classes and formal machine models, the Turing Machine model of computation, theories of computability, resource-bounded models, and NP-completeness. It is recommended that students have a B- or better in COMPSCI 311 in order to attempt COMPSCI 501.

Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.

Basic concepts of discrete mathematics useful to computer science: set theory, strings and formal languages, propositional and predicate calculus, relations and functions, basic number theory. Induction and recursion: interplay of inductive definition, inductive proof, and recursive algorithms. Graphs, trees, and search. Finite-state machines, regular languages, nondeterministic finite automata, Kleene's Theorem.

An in-depth examination of principles of distributed operating systems. Topics include processes and threads, concurrent programming, distributed interprocess communication, distributed process scheduling, shared virtual memory, distributed file systems. MACH. Familiarity with an undergraduate course on operating systems (CMPSCI 377 or equivalent) is helpful.

Introduction to computer communication networks and protocols. Fundamental concepts in the design and analysis of computer networks. Topics include: layered network architectures, networked applications, network programming interfaces, transport, congestion, routing, data link protocols, local area and data center networks, network security, and wireless networks. Examples drawn from the Internet (e.g., TCP, UDP, and IP) protocol suite.

The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results.

The World Wide Web was proposed originally as a collection of static documents inter-connected by hyperlinks. Today, the web has grown into a rich platform, built on a variety of protocols, standards, and programming languages, that aims to replace many of the services traditionally provided by a desktop operating system. This course will study core technologies, concepts, and techniques behind the creation of modern web-based systems and applications. This course satisfies the Integrative Experience requirement for CS and INFORM Majors.

Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability -- basic counting problems, probability definitions, mean, variance, binomial distribution, discrete random variables, continuous random variables, Markov and Chebyshev bounds, Laws of large number, and central limit theorem. Probabilistic reasoning -- conditional probability and odds, Bayes' Law, Markov Chains, Bayesian Network, Markov Decision Processes.