Computer Science (CS-GY)

This course introduces database systems and their approach as a mechanism to model the real world. The course covers data models (relational, object-oriented), physical database design, query languages, query processing and optimization, as well as transaction management techniques. Implementation issues, object oriented and distributed databases also are introduced. | Prerequisites for Brooklyn Students: (CS-UY 2134 or CS-UY 1134) and (CS-UY 2124 or CS-UY 1124) (C- or better) and MA-UY 2314 | Prerequisites for Abu Dhabi Students: (ENGR-UH 3510 or CS-UH 1050) (C- or better) and CS-UH 1002 | Prerequisites for Shanghai Students: CSCI-SHU 210 (C- or better) and CSCI-SHU 2314

This course covers cryptographic systems. Topics: Capability and access control mechanisms, authentication models, protection models. Database and operating system security issues, mobile code, security kernels. Malicious code, Trojan horses and computer viruses. Security policy formation and enforcement enforcement, legal aspects and ethical aspects. | Prerequisite for Brooklyn Students: CS-UY 2214 | Prerequisite for CAS Students: CSCI-UA 201 | Prerequisite for Abu Dhabi Students: CS-UH 2010 or ENGR-AD 3511 | Prerequisite for Shanghai Students: CENG-SHU 202 | Co-requisite for ALL Students: CS-UY 3224

Exceptional students may elect to write a master’s thesis for which no more than 6 credits may be earned toward the degree. Such research should demonstrate adequately the student’s proficiency in the subject material. Also required: oral thesis defense before at least three professors, plus a formal, bound thesis volume. Thesis registration must be continuous. | Prerequisites: Graduate status and satisfactory grades in prescribed courses.

The dissertation is an original investigation of a computer-science problem. The dissertation must demonstrate creativity and include features of originality and utility worthy of publication in a recognized journal. Candidates must orally defend their dissertations successfully. Registration of 21 credits and continuous dissertation registration are required. | Prerequisites: Graduate standing, and Passing of a qualifying examination and approval of the Department of Computer Science and Engineering.

This course covers logic, sets, functions, relations, asymptotic notation, proof techniques, induction, combinatorics, discrete probability, recurrences, graphs, trees, mathematical models of computation and undecidability. | Corequisite: Graduate Standing.

This course reviews basic data structures and mathematical tools. Topics: Data structures, priority queues, binary search trees, balanced search trees. Btrees. Algorithm design and analysis techniques illustrated in searching and sorting: heapsort, quicksort, sorting in linear time, medians and order statistics. Design and analysis techniques: dynamic programming, greedy algorithms. Graph algorithms: elementary graph algorithms (breadth first search, depth first search, topological sort, connected components, strongly connected components), minimum spanning tree, shortest path. String algorithms. Geometric algorithms. Linear programming. Brief introduction to NP completeness. | Knowledge of Discrete Math equivalent to CS-GY 6003, and knowledge of Fundamental Data Structures. Prerequisites: Graduate Standing

This course covers techniques in advanced design and analysis. Topics: Amortized analysis of algorithms. Advanced data structures: binomial heaps, Fibonacci heaps, data structures for disjoint sets, analysis of union by rank with path compression. Graph algorithms: elementary graph algorithms, maximum flow, matching algorithms. Randomized algorithms. Theory of NPcompleteness and approach to finding (approximate) solutions to NPcomplete problems. Selected additional topics that may vary. | Knowledge of algorithms and data structures equivalent to CS-GY 6033. Prerequisite: Graduate standing.

This course offers students a practical, hands-on introduction to the growing field of "Data Science," and will equip them with the fundamental quantitative and computational analytics used to derive meaningful insight from large, real-world data. The focus of the course is problem-based and brings together skills across statistics, data mining and machine learning alongside effective visualization and communication. The course will equip students to solve data-driven problems from diverse domain applications, and with real-world data. Students are assumed to have an understanding of basic statistics and have experience in programming. | Prerequisite: Graduate Standing.

The course emphasizes the full software-engineering approach with alternative approaches. Technical emphasis is on requirements, design, development and modeling. Management issues include software cost estimating and project management. Understanding the processes applicable to the software development/integration cycle and maintenance along with technology changes on quality and development activities is highlighted. | Solid ability to program in Python. Some familiarity with Django is helpful. Please note: The course will not teach how to program in Python. The students are required to be able to do so already. Experience using version control systems (git in particular) is important. Prerequisite: Graduate standing.

This course broadly introduces database systems, including the relational data model, query languages, database design, index and file structures, query processing and optimization, concurrency and recovery, transaction management and database design. Students acquire hands-on experience in working with database systems and in building web-accessible database applications. | Knowledge of basic data structures and algorithms (search trees, hash tables, sorting and searching). Knowledge of principles of operating systems and of the client-server architecture. Basic familiarity with the UNIX operating systems. Programming proficiency. Prerequisites: Graduate standing.

This course introduces basic issues in operating systems. Topics: Threads, processes, concurrency, memory management, I/O Control and case studies. | Prerequisite: Graduate standing.

An introductory course on Information Visualization based on a modern and cohesive view of the area. Topics include visualization design, data principles, visual encoding principles, interaction principles, single/multiple view methods, item/attribute, attribute reduction methods, toolkits, and evaluation. 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. | Prerequisite: Graduate Standing.

Visual analytics combines interactive visual interfaces and information visualization techniques with automatic algorithms to support analytical reasoning through human-computer interaction. People use visual analytics tools and techniques to synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data, and to communicate their findings effectively for decision-making. This course will serve as an introduction to the science and technology of visual analytics and will include lectures on both theoretical foundations and application methodologies. The goals of this course are for students to develop a comprehensive understanding of this emerging, multidisciplinary field, and apply that understanding toward a focused research problem in a real-world application or a domain of personal interest. | Knowledge of Information Visualization equivalent to CS-GY 6313. Prerequisite: Graduate Standing.

This course covers the structures, notations and semantics of programming languages. 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. | Knowledge of C, Python, Linux. Prerequisites: Graduate standing.

Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. In this course, we will study the state of art in big data management: we will learn about algorithms, techniques and tools needed to support big data processing. In addition, we will examine real applications that require massive data analysis and how they can be implemented on Big Data platforms. The course will consist of lectures based both on textbook material and scientific papers. It will include programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data platforms, including Amazon AWS. Besides lectures given by the instructor, we will also have guest lectures by experts in some of the topics we will cover. Students should have experience in programming: Java, C, C++, Python, or similar languages, equivalent to two introductory courses in programming, such as “Introduction to Programming” and “Data Structures and Algorithms. | Knowledge of Python. Prerequisite: Graduate Standing.

This course introduces the fundamentals of computer graphics with hands-on graphics programming experiences. 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. | Knowledge of Data Structures and Algorithms, and be comfortable with C/C++ programming.. Prerequisites: Graduate standing.

Designing a successful interactive experience or software system takes more than technical savvy and vision--it also requires a deep understanding of how to serve people's needs and desires through the experience of the system, and knowledge about how to weave this understanding into the development process. This course introduces key topics and methods for creating and evaluating human-computer interfaces/digital user experiences. Students apply these practices to a system of their choosing (I encourage application to prototype systems that students are currently working on in other contexts, at any stage of development). The course builds toward a final write-up and presentation in which students detail how they tackled HCI/user experience design and evaluation of their system, and results from their investigations. Some experience creating/participating in the production of interactive experiences/software is recommended. | Knowledge of the design of user experiences and interfaces is desirable but not required. Prerequisite: Graduate Standing.

This course is about experimental game design. Design in this context pertains to every aspect of the game, and these can be broadly characterized as the game system, control, visuals, audio, and resulting theme. We will explore these aspects through the creation of a few very focused game prototypes using a variety of contemporary game engines and frameworks, high-level programming languages, and physical materials. This will allow us to obtain a better understanding of what makes games appealing, and how game mechanics, systems, and a variety of player experiences can be designed and iteratively improved by means of rapid prototyping and play-testing. The course combines the technology, design, and philosophy in support of game creation, as well as the real-world implementation and design challenges faced by practicing game designers. Students will learn design guidelines and principles by which games can be conceived, prototyped, and fully developed within a one-semester course, and will create a game from start to finish. The course is a lot of (team)work, but it’s also a lot of fun. Programming skills are helpful, but not a hard requirement. Artistic skills, or a willingness to learn them are a plus. | Prerequisite: (Graduate Standing AND CS-GY 6533) for SoE students OR (OART-UT 1600 and OART-UT 1605) for Game Center MFA students OR instructor permission.

This advanced course in computer and network security focuses on penetration testing and vulnerability analysis. It introduces methodologies, techniques and tools to analyze and identify vulnerabilities in stand-alone and networked applications. | Knowledge of Network Security equivalent to CS-GY 6823. Prerequisites: Graduate standing.

Artificial Intelligence (AI) is an important topic in computer science and offers many diversified applications. 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. | Knowledge of Data Structures and Algorithms. Prerequisite: Graduate standing.

An important goal of artificial intelligence (AI) is to equip computers with the capability of interpreting visual inputs. 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. | Knowledge of Data Structures and Algorithms, proficiency in programming, and familiarity with matrix arithmetic. Prerequisites: Graduate standing.

This course introduces data structures and algorithms for geometric data. Topics include intersection, polygon triangulation, linear programming, orthogonal range searching, point location, Voronoi diagrams, Delaunay triangulations, arrangements and duality, geometric data structures, convex hulls, binary space partitions, robot motion planning, quadtrees, visibility graphs, simplex range searching. | Knowledge of algorithms and data structures equivalent to CS-GY 6033. Prerequisite: Graduate Standing.

This course introduces the theory of computation. Topics: Formal languages and automata theory. Deterministic and non-deterministic finite automata, regular expressions, regular languages, context-free languages. Pumping theorems for regular and context-free languages. Turing machines, recognizable and decidable languages. Limits of computability: the Halting Problem, undecidable and unrecognizable languages, reductions to prove undecidability. Time complexity, P and NP, Cook-Levin theorem, NP completeness. | Prerequisites: Graduate standing and CS-GY 6003 (or instructor’s permission). | Knowledge of discrete math (equivalent to CS-GY 6003). Prerequisite: Graduate Standing.

This course gives a behind-the-scenes look into the algorithms and computational methods that make machine learning and data science work at large scale. How does a service like Shazam match a sound clip to a library of 10 million songs in under a second? How do scientists find patterns in terabytes of genetic data? How can we efficiently train neural networks with millions of parameters on millions of labeled images? We will address these questions and others by studying advanced algorithmic techniques like randomization, approximation, sketching, continuous optimization, spectral methods, and Fourier methods. Students will learn how to theoretically analyze and apply these techniques to problems in machine learning and data science. They will also have the opportunity to explore recent research in algorithms for data through a final project and optional reading group. This course is mathematically rigorous and is intended for graduate students or strong, advanced undergraduates. | Knowledge of machine learning (equivalent to CS-UY 4563, CS-GY 6923, or ECE-GY 6143), algorithms (equivalent to CS-UY 2413, CS-GY 6033, or CS-GY 6043), and linear algebra (equivalent to MA-UY 2034, 3044, or 3054). Prerequisite: Graduate Standing.

This course presents a system and management view of information security: what it is, what drives the requirements for information security, how to integrate it into the systems-design process and life-cycle security management of information systems. A second goal is to cover basic federal policies on government information security and methodologies. Topics include information-security risk management, security policies, security in the systems-engineering process, laws related to information security and management of operational systems. | Prerequisite: Graduate standing. *Online version available.

This course introduces Information Systems Security and covers cryptography, capability and access control mechanisms, authentication models, security models, operating systems security, malicious code, security-policy formation and enforcement, vulnerability analysis, evaluating secure systems. | Prerequisite: Graduate standing. *Online version available.

This course begins by covering attacks and threats in computer networks, including network mapping, port scanning, sniffing, DoS, DDoS, reflection attacks, attacks on DNS and leveraging P2P deployments for attacks. 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. The course examines operational security, including firewalls and intrusion-detection systems. Students read recent research papers on network security and participate in an important lab component that includes packet sniffing, network mapping, firewalls, SSL and IPsec. | Prerequisite: Graduate standing. * Online version available.

This course takes a top-down approach to computer networking. 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. Topics at the link layer include multiple-access protocols, ALOHA, CSMA/CD, Ethernet, CSMA/CA, wireless 802.11 networks and linklayer switches. The course includes simple quantitative delay and throughput modeling, socket programming and network application development and Ethereal labs. | Knowledge of Python and/or C. Prerequisite: Graduate standing.

This course examines Modern Cryptography from a both theoretical and applied perspective, with emphasis on “provable security” and “application case studies”. The course looks particularly at cryptographic primitives that are building blocks of various cryptographic applications. The course studies notions of security for a given cryptographic primitive, its various constructions and respective security analysis based on the security notion. The cryptographic primitives covered include pseudorandom functions, symmetric encryption (block ciphers), hash functions and random oracles, message authentication codes, asymmetric encryption, digital signatures and authenticated key exchange. The course covers how to build provably secure cryptographic protocols (e.g., secure message transmission, identification schemes, secure function evaluation, etc.), and various number-theoretic assumptions upon which cryptography is based. Also covered: implementation issues (e.g., key lengths, key management, standards, etc.) and, as application case studies, a number of real-life scenarios currently using solutions from modern cryptography. | Prerequisite: Graduate standing.

This course covers the basic technology underlying Web search engines and related tools. The main focus is on large-scale Web search engines (such as Google, Yahoo and MSN Search) and their underlying architectures and techniques. Students learn how search engines work and get hands-on experience in how to build search engines from the ground up. Topics are based on a reading list of recent research papers. Students must work on a course project and may have to present in class. | Prerequisite: Graduate standing

This course is an introduction to the field of machine learning, covering fundamental techniques for classification, regression, dimensionality reduction, clustering, and model selection. 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.g. text, audio, images). Students will learn to train and validate machine learning models and analyze their performance. | Knowledge of undergraduate level probability and statistics, linear algebra, and multi-variable calculus. Prerequisite: Graduate standing.

This course covers artificial intelligence techniques used with games. The course is an advanced course that presupposes a good understanding of standard AI techniques, and much of the course material will consists of recent research papers. While the course will cover recent methods for playing games, in particular for general game playing, it will also go beyond that application domain to cover methods for generating games and game content and for modeling players. Many of these methods are based on evolutionary computation, others on stochastic tree search, cellular automata or grammar expansion. Approximately the first half of the course will consist of lectures, and the second half of the group projects. | Prerequisites: Graduate Standing and CS-GY 6613 or similar introductory Artificial Intelligence courses.

This course provides an overview of deep neural network learning (covering mathematical foundations as well as example applications in NLP, computer vision, and reinforcement learning). Upon successful completion, students will be able to grasp the mathematical basics of deep learning, solve practical machine learning problems in applications, and implement software prototypes of deep learning solutions to these problems. | Prerequisites: (CS-GY 6923 or ECE-GY 6143 or equivalent graduate course) AND graduate Standing.

This course introduces information-technology professionals to the application of forensic science principles and practices for collecting, preserving, examining, analyzing and presenting digital evidence. The course includes selected topics from the legal, forensic and information-technology domains and uses lecture, laboratory and written projects to illustrate these topics. | Prerequisite: Graduate standing. *Online version available.

This course will explore various topics in Computer Science

This course addresses the design and implementation of secure applications. Concentration is on writing software programs that make it difficult for intruders to exploit security holes. The course emphasizes writing secure distributed programs in Java. The security ramifications of class, field and method visibility are emphasized. | Knowledge of Information, Security and Privacy equivalent to CS-GY 6813. Prerequisite: Graduate standing

This is a 1.5-credit special topics course for graduate students.

This course will explore various topics in Computer Science.

This course is primarily for advanced graduate students who wish to study in a specialized area under faculty supervision. Permission of the graduate director is required, as are regular meetings with the adviser. An examination or term report is required. | Prerequisite: Graduate status.

This course permits the student to perform research in computer science with a narrower scope than a master’s thesis. Acceptance of a student by a faculty adviser is required before registration. A project report and an oral examination on it are required. | Prerequisite: Graduate status.