COMPSCI 163. Graph Algorithms. 4 Units.

The basis of the undergraduate programs are a set of fundamental courses in mathematics and computer science, supplemented by general education courses from other academic disciplines. A premium is placed on both communication and quantitative skills. Students quickly gain hands-on experience with advanced computing systems, and intense use of computer and network technologies continues throughout the undergraduate program. Students study data organization, algorithm design and analysis, design and organization of hardware and network systems, software engineering, artificial intelligence, social aspects of system design and use, and management of technology. In the process, students work with state-of-the-art hardware and software technologies, and learn several contemporary programming languages.

COMPSCI 163. Graph Algorithms. 4 Units.

COMPSCI 179. Algorithms for Probabilistic and Deterministic Graphical Models. 4 Units.

COMPSCI 265. Graph Algorithms. 4 Units.

The mission of the undergraduate program in Computer Science is to develop students' breadth of knowledge across the subject areas of computer science, including their ability to apply the defining processes of computer science theory, abstraction, design, and implementation to solve problems in the discipline. Students take a set of core courses. After learning the essential programming techniques and the mathematical foundations of computer science, students take courses in areas such as programming techniques, automata and complexity theory, systems programming, computer architecture, analysis of algorithms, artificial intelligence, and applications. The program prepares students for careers in government, law, and the corporate sector, and for graduate study.

COMPSCI 265. Graph Algorithms. 4 Units.

Computer Science encompasses both theoretical and practical aspects of design, analysis, and implementation of computer systems, as well as applications of computing to numerous other fields. Core research areas include: (1) artificial intelligence and machine learning, (2) bioinformatics, (3) computer architecture, (4) embedded systems, (5) graphics and computer vision, (6) database systems and information management, (7) multimedia and gaming, (8) networks and distributed systems, (9) programming languages and compilers, (10) security, privacy and cryptography, (11) design and analysis of algorithms, and (12) scientific computing.

COMPSCI 179. Algorithms for Probabilistic and Deterministic Graphical Models. 4 Units.

C Programming C++ Programming Graphic Design

Prerequisite: B.S. degree in Computer Science or basic courses in algorithms and data structures, calculus, discrete math, linear algebra, symbolic logic.

As a result, this algorithm can simulate different vehicle ..

Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. Focuses on using OpenGL to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Course will cover fundamental understanding of light and color, as well as how it impacts computer displays and printers. Class will discuss more thoroughly how light interacts with the environment, constructing engineering models such as the BRDF, plus various simplifications into more basic lighting and shading models. Also covers ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class mini-project consists of building out a ray tracer to create visually compelling images. Starter codes and code bits will be provided to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted toward in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged. Prerequisites: , .

Graph definitions, representation methods, graph problems, algorithms, approximation methods, and applications.

Simulation of an algorithm: computer ..

Pseudorandomness is the widely applicable theory of efficiently generating objects that look random, despite being constructed using little or no randomness. Since psudorandom objects can replace uniformly distributed ones (in a well-defined sense), one may view pseudorandomness as an extension of our understanding of randomness through the computational lens. We will study the basic tools pseudorandomness, such as limited independence, randomness extractors, expander graphs, and pseudorandom generators. We will also discuss the applications of pseudrandomness to derandomization, cryptography and more. We will cover classic result as well as cutting-edge techniques. Prerequisites: and , or equivalents.

Graph definitions, representation methods, graph problems, algorithms, approximation methods, and applications.

Project Ideas & Seminar Topics

Focuses on algorithms for probabilistic reasoning using graphical models such as Bayesian Networks and Markov Networks that encode knowledge as local probabilistic relations. Tasks include finding most likely scenarios over a subset of variables, or updating posterior probability, given observations.

In computer science, binary space partitioning ..

Prerequisite: B.S. degree in Computer Science or basic courses in algorithms and data structures, calculus, discrete math, linear algebra, symbolic logic.