CSCI-GA.1120 Introduction to Programming
4 Points. Graduate-level. Fall.
Prerequisites: None
This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python. No knowledge of programming is assumed. Not open to graduate Computer Science, Information Systems, Mathematics or Scientific Computing students.
CSCI-GA.1121 Working with Data
4 Points. Graduate-level. Fall.
Prerequisites: None
Students study the principles of relational database design and learn to build, populate, manipulate and query databases using SQL on datasets relevant to their interests. Students will also explore data presentation through data visualization. Not open to Graduate Computer Science, Information Systems, Mathematics or Scientific Computing students.
CSCI-GA.1122 Web Development
4 Points. Graduate-level. Spring.
Prerequisites: None
This course uses a project-based learning approach towards the study of web technologies and web programming. Students study the principles of web design and each student builds one or more interactive websites based on content relevant to their scholarly interest in the humanities or social sciences. Not open to graduate Computer Science, Information Systems, Mathematics or Scientific Computing students. For registration, please contact digitalhumanities@nyu.edu.
CSCI-GA.1133 Intensive Introduction to Graduate Study in Computer Science I (PAC I)
4 Points. Graduate-level. Fall.
Prerequisites: None
An accelerated introduction to the fundamental concepts of computer science for students who lack a formal background in the field. Topics include algorithm design and program development; data types; control structures; subprograms and parameter passing; recursion; data structures; searching and sorting; dynamic storage allocation and pointers; abstract data types, such as stacks, queues, lists, and tree structures; generic packages; and an introduction to the principles of object-oriented programming. The primary programming language used in the course will be Java. Students should expect an average of 12-16 hours of programming and related course work per week.
CSCI-GA.1144 Intensive Introduction to Graduate Study in Computer Science II (PAC II)
4 Points. Graduate-level. Spring.
Prerequisites: CSCI-GA 1133 or departmental permission.
This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms. Topics include: (1) Assembly language programming for the Intel chip family, emphasizing computer organization, the Intel x86 instruction set, the logic of machine addressing, registers and the system stack. (2) Programming in the C language, a general-purpose programming language which also has low-level features for systems programming. (3) An introduction to algorithms, including searching, sorting, graph algorithms and asymptotic complexity. Examples and assignments reinforce and refine those first seen in PAC I and often connect directly to topics in the core computer science graduate courses, such as Programming Languages, Fundamental Algorithms, and Operating Systems
CSCI-GA.1170 Fundamental Algorithms
3 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.
CSCI-GA.1180 Mathematical Techniques for Computer Science Applications
3 Points. Graduate-level. Fall.
Prerequisites: None
An introduction to theory, computational techniques, and applications of linear algebra, probability and statistics. These three areas of continuous mathematics are critical in many parts of computer science, including machine learning, scientific computing, computer vision, computational biology, natural language processing, and computer graphics. The course teaches a specialized language for mathematical computation, such as Matlab, and discusses how the language can be used for computation and for graphical output. No prior knowledge of linear algebra, probability, or statistics is assumed.CSCI-GA.2110 Programming Languages
3 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: Students taking this class should already have substantial programming experience.
Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.
CSCI-GA.2112 Scientific Computing
3 Points. Graduate-level. Fall.
Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
Methods for numerical applications in the physical and biological sciences, engineering, and finance. Basic principles and algorithms; specific problems from various application areas; use of standard software packages.CSCI-GA.2130 Compiler Construction
3 Points. Graduate-level. Fall, Spring.
Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
This is a capstone course based on compilers and modern programming languages. The topics covered include structure of one-pass and multiple-pass compilers; symbol table management; lexical analysis; traditional and automated parsing techniques, including recursive descent and LR parsing; syntax-directed translation and semantic analysis; run-time storage management; intermediate code generation; introduction to optimization; and code generation. The course includes a special compiler-related capstone project, which ties together concepts of algorithms, theory (formal languages), programming languages, software engineering, computer architecture, and other subjects covered in the MS curriculum. This project requires a substantial semester-long programming effort, such as construction of a language compilation or translation system that includes lexical and syntactic analyzers, a type checker, and a code generator.CSCI-GA.2180 Financial Software Projects
3 Points. Graduate-level. Fall.
Prerequisites: It is assumed that the students can code in C++. No prior experience in the financial sector domain is required.
The theme of this course is an "applied case study" and focuses on fixed income markets. Topics covered include an overview of the markets, the inner workings of an investment bank, the market players, and where software engineers fit in. Students will be grouped into small teams to build a financial application using practical software engineering principles. Each team will build a risk management framework, starting with basic components.CSCI-GA.2246 Open Source Tools
3 Points. Graduate-level. Fall.
Prerequisites: An understanding of modern operating systems and a working knowledge of a programming language, such as C, C++ or Java
This course covers a brief history and philosophy of open source software, followed by an in-depth look at open source tools intended for developers. In particular, we will present an overview of the Linux operating system, command line tools (find, grep, sed), programming tools (GIT, Eclipse, DTrace), web and database tools (Apache, MySQL), and system administration tools. We will also cover scripting languages such as shell and Python.CSCI-GA.2250 Operating Systems
3 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: None
The topics covered include a review of linkers and loaders and the high-level design of key operating systems concepts such as process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging and segmentation; and I/O and file systems, with examples from Unix/Linux and Windows. Programming assignments may require C, C++, Java, or C#.CSCI-GA.2262 Data Communications and Networks
3 Points. Graduate-level. Fall, Spring.
Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
This course teaches the design and implementation techniques essential for engineering robust networks. Topics include networking principles, Transmission Control Protocol/Internet Protocol, naming and addressing (Domain Name System), data encoding/decoding techniques, link layer protocols, routing protocols, transport layer services, congestion control, quality of service, network services, programmable routers and overlay networks.
CSCI-GA.2270 Computer Graphics
3 Points. Graduate-level. Fall.
Prerequisites: CSCI-GA 1170 and CSCI-UA 140 (or an equivalent undergraduate course in linear algebra).
Problems and objectives of computer graphics. Vector, curve, and character generation. Interactive display devices. Construction of hierarchical image list. Graphic data structures and graphics languages. Hidden-line problems; windowing, shading, and perspective projection. Curved surface generation display.
CSCI-GA.2271 Computer Vision
3 Points. Graduate-level. Fall.
Prerequisites: CSCI-GA 1170
Basic techniques of computer vision and image processing. General algorithms for image understanding problems. Study of binary image processing, edge detection, feature extraction, motion estimation, color processing, stereo vision, and elementary object recognition. Mathematical, signal processing, and image processing tools. Relation of computer vision algorithms to the human visual system.
CSCI-GA.2274 Advanced Computer Graphics
3 Points. Graduate-level. Fall.
Prerequisites: CSCI-GA 1170, CSCI-GA 2110, CSCI-GA 2250 and CSCI-GA 2270
This is a capstone course based on computer graphics tools. The course covers a selection of topics that may include computer animation, gaming, geometric modeling, motion capture, computational photography, physically based simulation, scientific visualization, and user interfaces. Not all areas are available every semester; the choice of areas is determined by the instructor. The capstone project involves some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.
CSCI-GA.2340 Elements of Discrete Mathematics
3 Points. Graduate-level. Summer.
Prerequisites: May not be taken by students who have received a grade of B or better in CSCI-GA 1170.
Introduction to the central mathematical concepts that arise in computer science. Emphasis is on proof and abstraction. Topics include proof techniques; combinatorics; sets, functions, and relations; discrete structures; order of magnitude analysis; formal logic; formal languages and automata.CSCI-GA.2390 Logic in Computer Science
3 Points. Graduate-level. Fall.
Prerequisites: Strong mathematical background and instructor permission for master’s students
A beginning graduate-level course in mathematical logic with motivation provided by applications in computer science. There are no formal prerequisites, but the pace of the class requires that students can cope with a significant level of mathematical sophistication. Topics include propositional and first-order logic; soundness, completeness, and compactness of first-order logic; first-order theories; undecidability and Godel’s incompleteness theorem; and an introduction to other logics such as second-order and temporal logic.CSCI-GA.2420 Numerical Methods I
3 Points. Graduate-level. Fall.
Prerequisites: Corequisite: linear algebra.
Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.CSCI-GA.2421 Numerical Methods II
3 Points. Graduate-level. Spring.
Prerequisites: Corequisite: linear algebra.
Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.CSCI-GA.2433 Database Systems
3 Points. Graduate-level. Fall, Spring.
Prerequisites: None
Database system architecture. Modeling an application and logical database design. The relational model and relational data definition and data manipulation languages. Design of relational databases and normalization theory. Physical database design. Concurrency and recovery. Query processing and optimization.CSCI-GA.2434 Advanced Database Systems
3 Points. Graduate-level. Fall.
Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
This is a capstone course emphasizing large-scale database systems. This course studies the internals of database systems as an introduction to research and as a basis for rational performance tuning. Topics include concurrency control, fault tolerance, operating system interactions, query processing, and principles of tuning. Database capstone projects involve topics such as design, concurrency control, interactions, and tuning. These projects include some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.CSCI-GA.2440 Software Engineering
3 Points. Graduate-level. Spring.
Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250
This is a capstone course focusing on large-scale software development. This course presents modern software engineering techniques and examines the software life cycle, including software specification, design, implementation, testing, and maintenance. Object-oriented design methods are also considered. Software engineering projects involve creation of a large-scale software system and require some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.CSCI-GA.2520 Bioinformatics and Genomes
4 Points. Graduate-level. Spring.
Prerequisites: None
The recent explosion in the availability of genome-wide data such as whole genome sequences and microarray data led to a vast increase in bioinformatics research and tool development. Bioinformatics is becoming a cornerstone for modern biology, especially in fields such as genomics. It is thus crucial to understand the basic ideas and to learn fundamental bioinformatics techniques. The emphasis of this course is on developing not only an understanding of existing tools but also the programming and statistics skills that allow students to solve new problems in a creative way.CSCI-GA.2560 Artificial Intelligence
3 Points. Graduate-level. Fall.
Prerequisites: None
There are many cognitive tasks that people do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course covers problem solving and state space search; automated reasoning; probabilistic reasoning; planning; and knowledge representation.CSCI-GA.2565 Machine Learning
3 Points. Graduate-level. Fall.
Prerequisites: Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Recommended: knowledge of vector calculus, elementary statistics, and probability theory.
This course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. The course covers the mathematical methods and theoretical aspects but primarily focuses on algorithmic and practical issues.CSCI-GA.2566 Foundations of Machine Learning
3 Points. Graduate-level. Fall.
Prerequisites: CSCI-GA 1180.
This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are probability and general bounds; PAC model; VC dimension; perceptron, Winnow; support vector machines (SVMs); kernel methods; decision trees; boosting; regression problems and algorithms; ranking problems and algorithms; halving algorithm, weighted majority algorithm, mistake bounds; learning automata, Angluin-type algorithms; and reinforcement learning, Markov decision processes (MDPs).
CSCI-GA.2567 Machine Learning
3 Points. Graduate-level. Spring.
Prerequisites: DS-GA-1001: Intro to Data Science or its equivalent, DS-GA-1002: Statistical and Mathematical Methods or its equivalent. Solid mathematical background, equivalent to a 1-semester undergraduate course in each of the following: linear algebra, multivariate calculus (primarily differential calculus), probability theory, and statistics. (The coverage in DS-GA 1002 is sufficient.) Python programming required for most homework assignments. Recommended: Computer science background up to a "data structures and algorithms" course. Recommended: At least one advanced, proof-based mathematics course
The course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. It covers the mathematical methods and theoretical aspects, but primarily focuses on algorithmic and practical issues.
Cross-listing for DS-GA 1003.
CSCI-GA.2569 Inference and Representation
3 Points. Graduate-level. Fall.
Prerequisites: DS-GA 1004
This course covers graphical models, causal inference, and advanced topics in statistical machine learning.
CSCI-GA.2572 Deep Learning
3 Points. Graduate-level. Spring.
Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
CSCI-GA.2580 Web Search Engines
3 Points. Graduate-level. Fall, Spring.
Prerequisites: Recommend CSCI-GA 1180.
Discusses the design of general and specialized Web search engines and the extraction of information from the results of Web search engines. Topics include Web crawlers, database design, query language, relevance ranking, document similarity and clustering, the “invisible” Web, specialized search engines, evaluation, natural language processing, data mining applied to the Web, and multimedia retrieval.
CSCI-GA.2585 Speech Recognition
3 Points. Graduate-level. Spring.
Prerequisites: Familiarity with basics in linear algebra, probability and analysis of algorithms. No specific knowledge about signal processing or other engineering material is required.
This course gives a computer science presentation of automatic speech recognition, the problem of transcribing accurately spoken utterances, and presents algorithms for creating large-scale speech recognition systems. The algorithms and techniques presented are now used in most research and industrial systems. The objective of the course is not only to familiarize students with particular algorithms used in speech recognition, but also to use that as a basis to explore general concepts of text and speech, as well as machine learning algorithms relevant to a variety of other areas in computer science. The course will make use of several software libraries and will study recent research and publications in this area.
CSCI-GA.2590 Natural Language Processing
3 Points. Graduate-level. Spring.
Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.
An introduction to natural language processing, with an emphasis on methods for creating structured knowledge from text. Basic syntactic structures of English; constituent and dependency representations and parsers. Hidden Markov Models and maximum entropy models for part-of-speech and name tagging. Finite-state grammars and partial parsing. Probabilistic parsing. Lexical semantics. Relation and event extraction. Reference resolution. Machine translation. Supervised, semi-supervised, and active learning of linguistic models. Neural network models. There will be several pencil-and-paper exercises, several computer exercises, a final project, and a final exam.
CSCI-GA.2591 Advanced Topics in Natural Language Processing
3 Points. Graduate-level. Fall.
Prerequisites: Students should have solid programming skills in Java and should have successfully completed a graduate level course in natural language processing.
The design of systems that can learn by reading. Ontology and knowledge base. Principal processing components: text segmentation, lexical analysis, name recognition and classification, parsing and syntactic regularization, word sense disambiguation, predicate-argument analysis, reference resolution, discourse structure. Joint inference methods. Knowledge acquisition strategies.
This is a project-oriented, team-organized course in which students will take responsibility for one or two processing components: selecting methods, making class presentations, creating and maintaining code. A good working knowledge of corpus-trained methods for these components is required (using log-linear or deep models). Implementation will be in Java, so good programming skills in Java are needed, along with a commitment to meeting javadoc documentation standards.
The tentative target is to read articles from Wikipedia.
CSCI-GA.2620 Networks and Mobile Systems
3 Points. Graduate-level. Spring.
Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
This is a capstone course. A course in computer networks and large-scale distributed systems. Teaches the design and implementation techniques essential for engineering both robust networks and Internet-scale distributed systems. The goal is to guide students so they can initiate and critique research ideas in networks and distributed systems and implement and evaluate a working system that can handle a real-world workload. Topics include routing protocols, network congestion control, wireless networking, peer-to-peer systems, overlay networks and applications, distributed storage systems, and network security.
CSCI-GA.2631 Distributed Computing
3 Points. Graduate-level.
Prerequisites: CSCI-GA 1170 and CSCI-GA 2250
Concepts underlying distributed systems: synchronization, communication, fault tolerance, and performance. Examined from three points of view: (1) problems, appropriate assumptions, and algorithmic solutions; (2) linguistic constructs; and (3) some typical systems.CSCI-GA.2930 Advanced Topics in Applied Mathematics
3 Points. Graduate-level. Fall.
Prerequisites: Topics determine prerequisites.
Topics vary each semester.CSCI-GA.2945 Advanced Topics in Numerical Analysis
3 Points. Graduate-level. Fall, Spring.
Prerequisites: Topics determine prerequisites.
Topics vary each semester.CSCI-GA.2965 Heuristic Problem Solving
3 Points. Graduate-level. Fall.
Prerequisites: CSCI-GA 1170 and an ability to prototype algorithms rapidly.
This course revolves around several problems new to computer science (derived from games or puzzles in columns for Dr. Dobb’s Journal, Scientific American, and elsewhere). The idea is to train students to face a new problem, read relevant literature, and come up with a solution. The solution entails winning a contest against other solutions. The winner receives candy. The best solutions become part of an evolving “Omniheurist” Web site that is expected to get many visitors over the years. The course is for highly motivated, mathematically adept students. It is open to supported Ph.D. students and well-qualified master’s students. Class size has been around 10 in the past, and instructor and students have all gotten to know one another very well. Algorithmic and programming knowledge is the main prerequisite. It also helps to be familiar with a rapid prototyping language such as Matlab, Mathematica, K, or Python, or to be completely fluent in some other language.CSCI-GA.3033 Special Topics in Computer Science
3 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: Prerequisites vary according to topic.
Topics vary each semester.
CSCI-GA.3110 Honors Programming Languages
4 Points. Graduate-level. Spring.
Prerequisites: Permission of the instructor for master’s students
The course will introduce a panorama of programming languages concepts underlying the main programming language paradigms (such as imperative, functional, object-oriented, logic, concurrent, and scripting languages) and present in detail the formal methods (code semantics, specification, and verification) used in modern high quality assurance tools for software safety and security. A programming project (design and implementation of an interpreter/compiler for an dynamic object-oriented mini-language) will be programmed in OCaml, a multiparadigm language introduced at the beginning of the course.CSCI-GA.3130 Honors Compilers and Computer Languages
4 Points. Graduate-level.
Prerequisites: Permission of the instructor for master’s students
Lexical scanning and scanner generation from regular expressions; LL, LR, and universal parser generation from context-free grammars; syntax-directed translation and attribute grammars; type and general semantic analysis; code generation, peephole optimization, and register allocation; and global program analysis and optimization. Provides experience using a variety of advanced language systems and experimental system prototypes.CSCI-GA.3205 Applied Cryptography and Network Security
3 Points. Graduate-level. Spring.
Prerequisites: None
This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.CSCI-GA.3210 Introduction to Cryptography
3 Points. Graduate-level. Fall.
Prerequisites: Strong mathematical background
The primary focus of this course is on definitions and constructions of various cryptographic objects, such as pseudorandom generators, encryption schemes, digital signature schemes, message authentication codes, block ciphers, and others, time permitting. The class tries to understand what security properties are desirable in such objects, how to properly define these properties, and how to design objects that satisfy them. Once a good definition is established for a particular object, the emphasis will be on constructing examples that provably satisfy the definition. Thus, a main prerequisite of this course is mathematical maturity and a certain comfort level with proofs. Secondary topics, covered only briefly, are current cryptographic practice and the history of cryptography and cryptanalysis.CSCI-GA.3220 Advanced Cryptography
3 Points. Graduate-level. Spring.
Prerequisites: CSCI-GA 3210.
Basics of computational number theory for cryptography. Identification protocols. Digital signatures. Public-key encryption. Additional selected topics.CSCI-GA.3230 Random Graphs
3 Points. Graduate-level.
Prerequisites: None
This course covers numerous topics related to random graphs, including generalized randomized structures, random processes, probabilistic methods and Erdös Magic. Also covered are branching processes, phase transitions for large random evolutions, derandomization via conditional expectations and semidefinite programming derandomization techniques. Algorithms, probability and discrete mathematics all appear, but concepts will be defined from scratch. Emphasis will be on methods of asymptotic calculation.CSCI-GA.3250 Honors Operating Systems
4 Points. Graduate-level.
Prerequisites: Permission of the instructor for master’s students
Operating-system structure. Processes. Process synchronization. Language mechanisms for concurrency. Deadlocks: modeling, prevention, avoidance, and recovery. Memory management. File-system interface. Secondary storage. Distributed systems: layered system design, managing distributed processes, distributed shared memory, fault-tolerance. CPU scheduling. Queuing and performance: analysis of single M/M/1 queue and others. Protection and security. Advanced security concepts: threat monitoring, encryption, and public keys.CSCI-GA.3520 Honors Analysis of Algorithms
4 Points. Graduate-level. Fall.
Prerequisites: Permission of the instructor for master’s students.
Design of algorithms and data structures. Review of searching, sorting, and fundamental graph algorithms. In-depth analysis of algorithmic complexity, including advanced topics on recurrence equations and NP-complete problems. Advanced topics on lower bounds, randomized algorithms, amortized algorithms, and data structure design as applied to union-find, pattern matching, polynomial arithmetic, network flow, and matching.CSCI-GA.3812 Information Technology Projects
3 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: For MS in IS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms and two of the following three courses: CSCI-GA 2262 Data Communications & Networks; CSCI-GA 2250 Operating Systems; CSCI-GA 2433 Database Systems. For MS in CS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms, CSCI-GA 2110 Programming Languages, and CSCI-GA 2250 Operating Systems. PERMISSION OF THE DEPARTMENT REQUIRED.
This is a capstone course that connects students directly with real-world information technology problems. The goal of this course is to teach the skills needed for success in real-world information technology via a combination of classroom lectures and practical experience with large projects that have been specified by local “clients.” The typical clients are primarily companies, but can also be government agencies or nonprofit organizations. Each project lasts for the entire semester and is designed to involve the full software project life cycle. Examples of such projects are development of software to solve a business problem, including specifying requirements, writing and testing prototype code, and writing a final report; and evaluation of commercial software to be purchased to address a business problem, including gathering requirements, designing an architecture to connect the new software with existing systems, and assessing the suitability of available software products.
CSCI-GA.3813 Advanced Laboratory
1-3 (MS), 1-12 (PhD) Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
Large-scale programming project or research in cooperation with a faculty member.
CSCI-GA.3840 Master’s Thesis Research
3 - 6 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
CSCI-GA.3850 Ph.D. Research Seminar
1 Points. Graduate-level. Fall, Spring.
Prerequisites: Permission of the instructor.
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.CSCI-GA.3860 Ph.D. Thesis Research
1 - 12 Points. Graduate-level. Fall, Spring.
Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870 Internship In Computer Science
1-3 Points. Graduate-level. Fall, Spring, Summer.
Prerequisites: Permission of Director of Graduate Studies.
Participation in a programming project or research project conducted outside the university in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may repeat this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.
Section 1 is for Master's students. Section 2 is for Ph.D. students.
MAINT-GA.4747 Maintenance of Matriculation
Points. Graduate-level. Fall, Spring.
Prerequisites: None
Section 1 is for MS students. Section 4 is for non-supported PhD students.