Operating systems concepts, including multi-threading, scheduling, synchronization, communication, and access control. Projects will cover design and implementation of several operating systems components.

Interpreters as abstract machines and the tools used to construct them, such as scanners and parsers. An introduction to models of computation as embodied by different programming languages. Limits of and relationships between these models.

Introduction to problems in computer networking, including error recovery, medium access, routing, flow control, and transport. Emphasis on current best practice. Includes programming of networked applications.

Geometric constuctions; transformations; perception; reflection models; photorealistic; non-photorealistic, and image-based rendering; rendering software and API's; triangle-mesh processing; graphic acceleration; user-interaction, design and animation.

An introduction to artificial intelligence and machine learning. Topics include intelligent system design methodologies, search and problem solving, supervised and reinforced learning.

Terms/concepts, threats, controls; problem definition; comprehensive information security model; security for operating systems, databases, network/distributed systems; administering security; legal/ethical/policy issues. Credit not allowed for both CS 4235 and CS 6035.

Comprehensive coverage of mainstream database concepts such as the entity-relationship model, relational databases, query languages, and database design methodology. Includes a project.

Motion techniques for computer animation and interactive games (keyframing, procedural methods, motion capture, and simulation) and principles for storytelling, composition, lighting, and interactivity.

Approximation algorithms for NP-hard optimization problems, design and analysis techniques for such algorithms.

Efficient randomized algorithms with improved performance over deterministic algorithms, or for NP-hard optimization problems, design and analysis techniques for such algorithms.

Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

Introduction to resource-bounded computations, central complexity-theoretic concepts such as complexity classes, reducibility, completeness, and intractability.

Introduction to linear error correcting codes with an emphasis on the algebraic tools required, including matrices vector spaces, groups, polynomial rings, and finite fields.

The fundamentals of graph theory: trees, connectivity, Euler torus, Hamilton cycles, matchings, colorings, and Ramsey theory.

This course develops in the theme of 'Arithmetic congruence and abstract algebraic structures'. Strong emphasis on theory and proofs.

Primes and unique factorization, congruences, Chinese remainder theorem, Diophantine equations, Diophantine approximations, quadratic reciprocity. Applications such as fast multiplication, factorization, and encryption.

Probability distributions, limit laws, and applications through the computer.

The measurement and quantification of information. These ideas are applied to the probabilistic analysis of the transmission of information over a channel along which random distortion of the message occurs.

Finite dimensional vector spaces, inner product spaces, least squares, linear transformations, the spectral theorem for normal transformations. Applications to convex sets, positive matrices, difference equations.

A study of linear programming problems, including the simplex method, duality, and sensitivity analysis with applications to matrix games, integer programming, and networks.

Introduction to numerical algorithms for some basic problems in computational mathematics. Discussion of both implementation issues and error analysis.

Introduction to quantum computing and quantum information theory, formalism of quantum mechanics, quantum gates, algorithms, measurements, coding, and information. Physical realizations and experiments. Crosslisted with PHYS 4782.

Design, analysis and implementation of algorithms for modeling natural and engineered systems; algorithm experimentation, and optimization.

Algorithms and techniques for creating computer simulations and their realization in software.