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Looking for News or Events? Visit NYU Returns for campus information. Request Information. We offer a highly adaptive MS in Computer Science program that lets you shape the degree around your interests.
Besides our core curriculum in the fundamentals of computer science, you have a wealth of electives to choose from. You can focus on such topics as computer and network security, distributed systems and networking, computer graphics, and web search technology, along with subjects outside the department.
Job opportunities in computer science are challenging and diverse, and we expect to see steady demand for highly qualified graduates at all levels. As a graduate, you can explore a number of possible occupations, including applications programmer, database manager, systems administrator, or IT analyst.
With our MS program in Computer Science, you will have maximum curriculum flexibility, allowing you to adapt your program to your ambitions and goals as well as to your educational and professional backgrounds. Not only will you gain a solid grounding in the fundamentals of computer science, but professional-level courses, and an opportunity to specialize in selected technology areas of your choice.
Admission to this program requires you to masters thesis papers on compiler construction an undergraduate degree in computer sciencemathematicsscience, or engineeringwith a superior undergraduate record from an accredited institution.
Applicants with degrees in other fields are considered individually for admission. Find out more about general Admission Requirements. Generally, entering students are expected to know mathematics through calculus, masters thesis papers on compiler construction.
Admission with advanced standing is accepted in accordance with the School of Engineering regulations. A maximum of 9 credits may be applied to the MS degree from previous graduate work at an acceptable institution. Students who are lacking the computer science skills needed for the Computer Science Master's Degree are encouraged to enroll into the preparatory Bridge to NYU Tandon program.
Pending satisfactory completion, students would be considered for admission towards the master's degree program. Masters thesis papers on compiler construction who satisfy one of the following conditions are not required but encouraged to submit a GRE score:. In addition, a B average is required across the required algorithms course and the four core courses, and a grade of B or better is required for the capstone course, as indicated below.
Most students will take the Algorithms I course to satisfy the algorithms course requirement. Advanced students who have taken an equivalent Algorithms I course before with a grade of B or better will have the option of taking the Algorithms II course to satisfy the requirement. Students must take at least four courses from the list of core courses below. The list will be periodically updated by the CSE Department and certain courses may be substituted with departmental consent.
Technical emphasis is on requirements, design, development and modeling. Management issues include software cost estimating and project management. Prerequisite: Graduate standing. Students acquire hands-on experience in working with database systems and in building web-accessible database applications. Prerequisites: Graduate standing, CS-GY or equivalent, familiarity with basic data structures and operating system principles. An instruction set studied in detail.
Processor implementations: Unpipelined execution and its improvement by means of pipelining. Advanced pipelining, including branch prediction, out-of-order execution and superscalar execution, is introduced. Alternatives to traditional computing, such as VLIW and vector computation are described. Improving computer capacity, by improving the memory hierarchy is studied, including advanced cache memory, main memory and virtual memory implementations.
An introduction to high-performance computing, including multi-core processors. Prerequisite: CS-UY and Graduate standing. Overviews and examples from state-of-the-art research will be provided.
The course is designed as a first course in information visualization for students both intending to specialize in visualization as well as students who are interested in understanding and applying visualization principles and existing techniques. Topics: Issues of scope, type structure and parameter passing.
Control structures, including support for exception handling and concurrency. Abstract data types and object oriented languages. Programming in the large. Implementation issues. Functional, logic programming languages. Examples from a variety of languages.
Prerequisites: Graduate standing and CS-GY Topics include graphics software and hardware, 2D line segment-scan conversion, 2D and 3D transformations, viewing, clipping, polygon-scan conversion, hidden surface removal, illumination and shading, compositing, texture mapping, ray tracing, radiosity and scientific visualization.
It addresses one of the ultimate puzzles humans are trying to solve: How is it possible for a slow, tiny brain, whether biological or electronic, to perceive, understand, predict and manipulate a world far larger and more complicated than itself?
And how do people create a machine or computer with those properties? To that end, AI researchers try to understand how seeing, learning, remembering and reasoning can, or should, be done. This course introduces students to the many AI concepts and techniques. Prerequisite: Graduate standing and CS-GY Computer vision is an area in AI that deals with the construction of explicit, meaningful descriptions of physical objects from images.
It includes as parts many techniques from image processing, pattern recognition, geometric modeling, and cognitive processing. This course introduces students to the fundamental concepts and techniques in computer vision. Prerequisites: Graduate standing, CS-GY and MA-UYor equivalents, or instructor's permission, masters thesis papers on compiler construction.
Prerequisite: Competency in Application Development in UNIX and Windows Environments, Graduate status. After an overview of computer networks and the Internet, the course covers the application layer, transport layer, network layer and link layers.
Topics at the application layer include client-server architectures, P2P architectures, DNS and HTTP and Web applications. Topics at the transport layer include multiplexing, connectionless transport and UDP, principles or reliable data transfer, connection-oriented transport and TCP and TCP congestion control. Topics at the network layer include forwarding, router architecture, the IP protocol and routing protocols including OSPF and BGP. The course includes simple quantitative delay and throughput modeling, masters thesis papers on compiler construction programming and network application development and Ethereal labs.
Prerequisite: Graduate standing and CS-UY A broad range of algorithms will be covered, such as linear and logistic regression, neural networks, deep learning, support vector machines, tree-based methods, expectation maximization, and principal components analysis. The course will include hands-on exercises with real data from different application areas e. text, audio, images. Students will learn to train and validate machine learning models and analyze their performance.
Prerequisite: Graduate status with masters thesis papers on compiler construction level probability theory 3 Credits Selected Topics in CS CS-GY Certain courses in our department will be designated as capstone courses.
Capstone courses are drawn from key technical areas in the MS program and they involve a substantial amount of programming effort.
Students are required to take at least one capstone course with a grade of B or better. The list of capstone courses will be posted by the department and will be updated from time to masters thesis papers on compiler construction. If a course is listed both as a capstone course and as a core course, the course can be used to satisfy both the capstone and core course requirements.
An MS thesis can also be used to satisfy the capstone course requirement, masters thesis papers on compiler construction. Important design and management issues are analyzed and evaluated. Technical and management tradeoffs in distributed software systems are emphasized.
Masters thesis papers on compiler construction extensive number of real world case studies are assessed. A class masters thesis papers on compiler construction is required. Prerequisite: CS-GY and graduate standing. Topics may include virtualization, masters thesis papers on compiler construction, network server design and characterization, scheduling and resource optimization, file systems, memory management, advanced debugging techniques, data-center design and energy utilization.
Topics: Distributed control and consensus. Notions of time in distributed systems. Distributed File Systems and Services. Fault tolerance, replication and transparency. Peer-to-peer systems. Case studies of modern commercial systems and research efforts.
Topics: Lexical analysis, syntax analysis, abstract syntax trees, symbol table organization, code generation. Introduction to code optimization techniques. Prerequisites: CS-GYCS-GY and CS-GY It introduces methodologies, techniques and tools to analyze and identify vulnerabilities in stand-alone and networked applications. Prerequisites: Graduate standing and CS-GY 3 Credits Artificial Intelligence I CS-GY Artificial Intelligence AI is an important topic in computer science and offers many diversified applications.
Topics: Discussion of organization and learning in neural network models including perceptrons, adalines, back-propagation networks, recurrent networks, adaptive resonance theory and the neocognitron. Implementations in general and special purpose hardware, masters thesis papers on compiler construction, both analog and digital.
Application in various areas with comparisons to nonneural approaches. Decision systems, nonlinear control, speech processing and vision.
Some familiarity with matrix notation and partial derivatives is recommended. The course continues with cryptography topics most relevant to secure networking protocols. Topics covered are block ciphers, stream ciphers, public key cryptography, RSA, Diffie Hellman, certification authorities, digital signatures and message integrity. After surveying basic cryptographic techniques, the course examines several secure networking protocols, including PGP, SSL, IPsec and wireless security protocols.
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