Course Schedule

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1122-001
(3537) Web Development**** Joshua Clayton
Office Hours T 6:20-9:00PM 60 Fifth Ave C03 Additional information: Not open to graduate Computer Science, Information Systems, CEI (Computing, Entrepreneurship, and Innovation)

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 dhss@nyu.edu.
CSCI-GA.1144-001
(2632) PAC II* Mohamed Zahran
Office Hours T 6:00-8:30PM CIWW 312

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

Prerequisites: CSCI-GA 1133 or departmental permission.
CSCI-GA.1144-002
(3039) PAC II Recitation Richik Jaiswal
Office Hours R 7:10-8:00PM CIWW 312

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

Prerequisites: CSCI-GA 1133 or departmental permission.
CSCI-GA.1170-001
DS-GA.1170
(2633) Fundamental Algorithms Alexander Alekseyev
Office Hours M 5:10-7:00PM Silv 405

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.

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).
CSCI-GA.1170-002
DS-GA.1170
(2634) Fundamental Algorithms Recitation Yassine Kadiri W 5:10-6:00PM CIWW 101

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.

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).
CSCI-GA.1170-003
DS-GA.1170
(3425) Fundamental Algorithms Yevgeniy Dodis
Office Hours T 7:10-9:00PM CIWW 109

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.

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).
CSCI-GA.1170-004
DS-GA.1170
(3536) Fundamental Algorithms Recitation Shravas Rao R 8:10-9:00PM CIWW 109

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.

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).
CSCI-GA.1180-001
(20913) Mathematical Techniques For CS Applications Remi Imbach
Office Hours T 5:10-7:00PM CIWW 317
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.2110-001
(2635) Programming Languages Thomas Wies
Office Hours W 7:10-9:00PM CIWW 109

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.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-002
(2636) Programming Languages Recitation Thomas Wies
Office Hours R 7:10-8:00PM CIWW 109

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.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2130-001
(2637) Compiler Construction Eva Rose
Office Hours M 7:10-9:00PM CIWW 202
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.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2250-001
(2638) Operating Systems Hubertus Franke
Office Hours M 7:10-9:00PM CIWW 109
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.2250-002
(3268) Operating Systems Shameem Akhter
Office Hours W 7:10-9:00PM CIWW 102
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.2262-001
(2639) Data Communications and Networks Jean-Claude Franchitti
Office Hours R 5:10-7:00PM CIWW 101

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.

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.
CSCI-GA.2421-001
MATH-GA 2020-001
(2640) Numerical Methods II Aleksandar Donev T 5:10-7:00PM CIWW 202
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.

Prerequisites: Corequisite: linear algebra.
CSCI-GA.2433-001
DS-GA.2433-001
(2641) Database Systems Zvi Kedem
Office Hours W 5:10-7:00PM CIWW 109
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.2440-001
(2642) Software Engineering Jean-Claude Franchitti
Office Hours M 7:10-9:00PM CIWW 317
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.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250
CSCI-GA.2520-001
BIOL-GA 1127-001
(2643) Bioinformatics and Genomes Brian Parker F 1:00-3:45PM CIWW 102
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.2560-001
(3538) Artificial Intelligence Ernest Davis
Office Hours M 5:10-7:00PM CIWW 102
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.2565-001
(20916) Machine Learning Rajesh Ranganath
Office Hours T 5:10-7:00PM CIWW 109
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.

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.
CSCI-GA.2572-001
DS-GA.1008-001
(23069) Deep Learning Yann LeCun M 4:55-6:35PM CIWW 109

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.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2572-002
DS-GA.1008-002
(23259) Deep Learning Lab Yann LeCun T 7:10-8:00PM Meyr 121

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.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2590-001
(3040) Natural Language Processing Ralph Grishman
Office Hours T 5:10-7:00PM 60 Fifth Ave 150

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.

Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.
CSCI-GA.2620-001
(3076) Networks and Mobile Systems Anirudh Sivaraman
Office Hours R 7:10-9:00PM 60 Fifth Ave C12

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.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2820-001
(20918) DevOps and Agile Methodologies* John Rofrano
Office Hours T 5:10-7:00PM CIWW 101 Additional information: Not open to graduate Computer Science, Information Systems, CEI (Computing, Entrepreneurship, and Innovation), or Non-Degree students.

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2820-002
(20919) DevOps and Agile Methodologies Lab* John Rofrano
Office Hours T 7:10-8:00PM CIWW 101 Additional information: Not open to graduate Computer Science, Information Systems, CEI (Computing, Entrepreneurship, and Innovation), or Non-Degree students.

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2840-001
(20920) Entrepreneurship Capstone Evan Korth
Office Hours T 10:00-12:00PM 16WP 106 Additional information: Open only to students in the MS-CEI program.

The Entrepreneurship Capstone class is a 15week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.
CSCI-GA.2840-002
(20945) Entrepreneurship Capstone Lab Nolan Filter T 12:10-1:00PM 16WP 106 Additional information: Open only to students in the MS-CEI program.

The Entrepreneurship Capstone class is a 15week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.
CSCI-GA.2945-001
MATH-GA.2012-001
(2644) Adv Topics Num Analysis: High Performance Computing Georg Stadler
Dhairya Malhotra M 5:10-7:00PM CIWW 1302

This class will be an introduction to the fundamentals of parallel scientific computing. We will establish a basic understanding of modern computer architectures (CPUs and accelerators, memory hierarchies, interconnects) and of parallel approaches to programming these machines (distributed vs. shared memory parallelism: MPI, OpenMP, OpenCL/CUDA). Issues such as load balancing, communication, and synchronization will be covered and illustrated in the context of parallel numerical algorithms. Since a prerequisite for good parallel performance is good serial performance, this aspect will also be addressed. Along the way you will be exposed to important tools for high performance computing such as debuggers, schedulers, visualization, and version control systems. This will be a hands-on class, with several parallel (and serial) computing assignments, in which you will explore material by yourself and try things out. There will be a larger final project at the end. You will learn some Unix in this course, if you don't know it already. Prerequisites for the course are (serial) programming experience with C/C++ (I will use C in class) or FORTRAN, and some familiarity with numerical methods.
CSCI-GA.2945-002
MATH-GA.2012-002
(2645) Adv Topics Num Analysis: Immersed Boundary Method Charles Peskin M 11:00-12:50PM CIWW 1302

The immersed boundary (IB) method is a general framework for the computer simulation of flows with immersed elastic boundaries and/or complicated geometry. It was originally developed to study the fluid dynamics of heart valves, and it has since been applied to a wide variety of problems in biofluid dynamics, such as wave propagation in the inner ear, blood clotting, fish swimming, and insect flight.

Non-biological applications include sails, parachutes, flows of suspensions, and two-fluid or multifluid problems. Topics to be covered include: mathematical formulation of fluid-structure interaction in Eulerian and Lagrangian variables with the Dirac delta function as the link between these two kinds of variables; discretization of the structure, fluid, and interaction equations, including energy-based discretization of the structure equations, finite difference discretization of the fluid equations, and IB delta functions with specified mathematical properties; an IB method with an exactly divergence-free interpolated velocity field; IB methods for immersed boundaries with non-trivial mass and for fluids with non-uniform density and viscosity; IB methods for immersed filaments with bend and twist; and a stochastic IB method for thermally fluctuating hydrodynamics within biological cells.

Course requirements include homework assignments and a computing project, but no exam. Students may collaborate on the homework and on the computing project, and are encouraged to present the results of their computing projects to the class.

Text:

The Immersed Boundary Method, lecture notes by Charles S. Peskin. These notes will be supplemented by selected publications on the topics of the course.

Prerequisites: Familiarity with numerical methods and fluid dynamics.
CSCI-GA.2945-003
MATH-GA.2012-003
(20917) Adv Topics Num Analysis: Numerical Optimization Margaret Wright
Office Hours R 5:10-7:00PM CIWW 312

Many problems in science, engineering, medicine, and business involve optimization, in which we seek to optimize a mathematical measure of goodness subject to constraints. This course will survey widely used methods for continuous optimization, focusing on both theoretical foundations and implementation as software.

The course will cover: linear programming (including the simplex method and interior-point methods); line search and trust region methods for unconstrained optimization; methods for convex and nonconvex quadratic programming; and penalty, barrier, augmented agrangian, and interior-point methods for convex and nonconvex constrained optimization. In all cases, attention will be paid to numerical issues.

Prerequisites: Students are expected to be comfortable with numerical linear algebra and multivariate calculus, and to have programming experience (preferably in Matlab). Students without all elements of this background are likely to have difficulty with the course material.
CSCI-GA.2945-004
MATH-GA.2012-004
(24010) Adv Topics Num Analysis: Stochastic modeling and uncertainity quantification in complex systems Benjamin Peherstorfer
Office Hours M 1:25-3:15PM CIWW 517

As physical and time domains of partial differential equations (PDEs) can be resolved with ever higher accuracy, the quality of numerical simulations of physical phenomena increasingly tends to be limited by noisy and incomplete data that insufficiently describe boundary conditions, coefficients, and other parameters of the problem setups. This course provides an introduction to the stochastic modeling of these data uncertainties as random coefficients and random forcing terms of PDEs and discusses dimensionality reduction methods for efficiently estimating moments of the solutions of the corresponding stochastic PDEs. The first part focuses on stochastic modeling and sampling (Karhunen-Loeve expansion, polynomial chaos expansion, stochastic Galerkin, (multilevel) Monte Carlo, stochastic collocation, sparse grids). The second part discusses dimensionality reduction techniques to make estimation of moments of the PDE solutions computationally tractable (proper orthogonal decomposition, a posteriori error estimation and error control of reduced models, empirical interpolation).
CSCI-GA.2945-005
MATH-GA.2012-005
(26174) Adv Topics Num Analysis: Monte Carlo Methods Jonathan Weare W 11:00-12:50PM CIWW 1302

This course primarily concerns the design and analysis of Monte Carlo sampling techniques for the estimation of averages with respect to high dimensional probability distributions. Standard simulation tools such as importance sampling, Gibbs and Metropolis-Hastings sampling, Langevin dynamics, and hybrid Monte Carlo will be introduced along with basic theoretical concepts regarding their convergence to equilibrium. The focus is on methods that are broadly useful for problems in science, engineering, and statistics. Particular attention will be paid to the major complicating issues like conditioning and rare events and methods to address them (e.g. tempering, interacting particle methods, and free-energy methods).
CSCI-GA.3033-001
(2646) Special Topics: Big Data Science Anasse Bari
Office Hours W 5:10-7:00PM CIWW 201

Data Science has recently gained a substantial interest in a variety of industries including healthcare, finance, manufacturing and marketing. It is positioned at the intersection point of computer science, data mining, machine learning, predictive analytics, business, statistics and domain expertise. It aims at finding hidden knowledge (patterns, models, associations) for the purpose of making better decisions. This course will introduce the basic fundamental concepts of the algorithms and the tools of extracting valuable insights from data with the ability to formulating and solving problems with it. Topics of this course include, but are not limited to: Basics of Data Analytics Project Lifecycle, Developing an Analytics Roadmap, Data Preprocessing, Data Analytics Use cases, Data Cleaning Algorithms, Feature Selection Algorithms, Introduction to Hadoop in Theory and in Practice, Algorithms for Finding Similar Data Items, Dimensionality Reduction techniques, Clustering Algorithms, Classification Algorithms, Introduction to Sentiment Analysis in short text (e.g Tweets), Mining Association Rules, Performance Measures, Ensemble Methods, Information retrieval systems and Recommendation Systems with applications in Bio-informatics, Text Mining and Social Network Analytics.

Prerequisites: Strong programming skills in the Java language and fundamentals of linear algebra.
CSCI-GA.3033-002
(3126) Special Topics: Social Networks Bhubaneswar Mishra
Office Hours R 7:10-9:00PM CIWW 512

Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. The World Wide Web enables information flows among vast number of humans; Facebook, and LinkedIn, etc. connect small groups of friends; amazon, eBay, etc. provide opportunities for trading, etc, BitCoins, Ethereum, etc. allow parties to make financial and contractual transactions . These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorumsensing, swarming, flocking, social grooming, gossip, etc., and have evolved with the dynamics of evolutionary games (particularly Signaling Games). Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant. Economic markets also look much more like networks than anonymous marketplaces. Firms interact with the suppliers and customers in a Web-like supply chains. Systemic risk in financial markets often results from the counterparty risks created within this financial network. This course will introduce the tools for the study of networks, and mechanism designed to interact strategically: SmartContractss, Costly Signaling, Credible and Noncredible Threats, Model Checking and Verification, Safety and Liveness, Recommenders and Verifiers, Deception, Market mechanisms (pricing and auction), Privacy and Trust… Exampled Systems: Bit-coins, Zcash, Ripple, Stellar, Ethereum, Solidity, Rholang, DAO (Distributed Companies), Cyber Security (M-coins), Information Finance Markets (WealthFront, etc.), Market Microstructure, ICO/STO/ITO, GDPR, OpenLaw, OpenZeplin, ...
CSCI-GA.3033-003
(2647) Special Topics: Production Quality Software CANCELLED -

In this course we study the craft of software engineering. The semester begins with a thorough review of Java and the Java Virtual Machine. We concentrate on library components and language features most relevant to typical engineering projects. Later lectures cover more sophisticated topics, including concurrency and thread-safety, writing tests, object-oriented programming, and design patterns. The course also covers non-technical topics that are critical to software development including how to write with clarity, managing bugs, successfully developing and reinforcing salutary engineering habits, the nature of human productivity as it relates specifically to engineering, and communicating effectively with teammates. Lessons are reinforced with weekly assignments that are meticulously graded, with some submissions critiqued in classroom code reviews.
CSCI-GA.3033-005
(2649) Special Topics: Big Data Application Development Suzanne McIntosh
Office Hours R 7:10-9:00PM CIWW 101

This course covers Scala, Spark programming, Spark architecture, Spark Streaming, and integration of Spark with the Hadoop ecosystem for developing Big Data applications. In addition, it covers the technologies that integrate well with Spark in the creation of Big Data applications. Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by developing a final project using Scala, Spark, and complementary Hadoop tools.

Prerequisites: Students should have experience using Hadoop (HDFS and MapReduce). In addition, students should have: strong programming skills in Java, Python, or C/C++; coursework in operating systems, networking, and algorithms; familiarity with Linux commands.
CSCI-GA.3033-008
(3136) Special Topics: .Net Web Application Development Cory Plock
Office Hours M 5:10-7:00PM CIWW 101

This course introduces the concepts and tools necessary for building robust web applications using the Microsoft .NET Framework. Among the topics explored will be the multi-language .NET architecture, build and deployment process, the C# programming language, Entity Framework, Model-View-Controller (MVC) design pattern, web services, Git version control, and the Azure cloud platform. This is a hands-on project-based course where students will build a fully functional web solution.

Prerequisites: Programming languages, object-oriented programming, relational databases, and data structures.
CSCI-GA.3033-010
(3354) Special Topics: Cloud Computing Jean-Claude Franchitti
Office Hours R 7:10-9:00PM CIWW 102

This is a capstone course.

This course provides a hands-on comprehensive study of Cloud concepts and capabilities across the various Cloud service models including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Business Process as a Service (BPaaS).

The IaaS topics start with a detailed study of the evolution of infrastructure migration approaches from VMWare/Xen/KVM virtualization, to adaptive virtualization, and Cloud Computing / on-demand resources provisioning. Mainstream Cloud infrastructure services and related vendor solutions are also covered in detail.

The PaaS topics cover a broad range of Cloud vendor platforms including AWS, Red Hat's OpenShift, Cloud Foundry, Apache Stratos, Microsoft Azure, Eucalyptus, OpenStack and others as well as a detailed study of related platform services such as storage capabilities that leverage Google Storage, Amazon S3, Amazon Dynamo, or other facilities meant to provide Cloud resources management and monitoring capabilities.

The course will focus in particular on mainstream Application Platform as a Service (aPaaS), which is a PaaS offering that leverages container services (e.g., Docker) and microservices architectures aimed at facilitating application development, deployment and execution in the cloud. In addition to encapsulating infrastructure resources, aPaaSs include services such as those for data management (including NoSQL and in-memory data services), BPM and decision-engine services, API Management for hybrid integration, and user interfaces. As an example, Cloud foundation offerings provided on top of modern aPaaS such as IBM's Bluemix Liberty for Java include a collection of Watson analytics, big data, mobile, security, IoT, integration, DevOps, Blockchain, as well as other functionality.

Similar functionalities are provided on the AWS, Google Cloud (GCP), and Microsoft Azure platforms. Additional aPaaS platforms of interest Oracle aPaaS, Red Hat OpenShift, Salesforce App Cloud, and SAP Hana Cloud Platform (HCP).
CSCI-GA.3033-016
(3356) Special Topics: Multicore Processors: Architecture and Programming Mohamed Zahran
Office Hours W 5:10-7:00PM CIWW 312

This is a capstone course.

The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable.
CSCI-GA.3033-018
(3361) Special Topics: Geometric Modeling Daniele Panozzo
Office Hours R 5:10-7:00PM 60 Fifth Ave C12

Recent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on polygonal meshes, surface reconstruction, mesh improvement, mesh parametrization, discrete differential geometry, interactive shape editing, skinning animation, architectural and structure-aware geometric modeling, shape modeling with an eye on 3D printing. The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.

Prerequisites: Basic knowledge of Computer Graphics, experience with C++ programming. Some background in geometric computing is helpful, but not necessary. CSCI-UA 140 (or an equivalent undergraduate course in linear algebra)
CSCI-GA.3033-019
(3362) Special Topics: Practical Computer Security Kevin Chen
Office Hours T 7:10-9:00PM CIWW 201

This course teaches the security mindset and practical security concepts that are essential for software engineers, software developers, quality assurance testers and IT managers. The course discusses various attack techniques and defenses for real world applications and the security activities in the SDLC. Its main topics include: low-level exploits, web application security, malware, threat modeling, security requirements, secure coding practices, security & penetration testing, human factors and usability, mobile platform security, and legal and ethical issues. Course project and assignments may require C, JavaScript, Python, and/or other scripting languages.

Prerequisites: Programming experience in C/C++, JavaScript, Python, and SQL; basic understanding of assembly language; working knowledge of web application development.
CSCI-GA.3033-020
DS-GA.3001-014
(3357) Special Topics: Mathematics of Deep Learning Joan Bruna M 7:10-9:00PM CIWW 102

This graduate-level course explores some of the open mathematical challenges arising in the study of Deep Learning. In particular, we will focus on three main topics: (i) signal processing principles underpinning convolutional neural networks and their generalisations to non-euclidean geometries (ii) non-convex optimization challenges arising in high-dimensional (or overparametrised) learning problems, and (iii) geometry of Markov Decision Processes and its role in Reinforcement Learning guarantees.

Prerequisites: Background on high-dimensional probability, statistics, and/or harmonic analysis. Familiarity with general Machine Learning topics and basic notions of optimization.
CSCI-GA.3033-023
(3725) Special Topics: High Performance Machine Learning Giacomo Domeniconi
Office Hours
Ulrich Finkler W 7:10-9:00PM CIWW 312

One of the key ingredients of the success of Machine Learning is the ability to perform computations on very large amounts of training data. High Performance Computing (HPC) is where cutting edge technology (GPUs, low latency interconnects, etc.) is applied to the solution of computing intensive problems. Today, the application of High Performance Computing techniques to ML algorithms is a fundamental driver for the progress of Artificial Intelligence. In this course, you will learn HPC techniques that are typically applied to supercomputing software, and how they are applied to obtain the maximum performance out of ML algorithms.

Topics: ML Hardware and Software stack, Performance analysis methodology, ML algorithms performance, CUDA, cuDNN, Pytorch, MPI, Parallel and Distributed ML algorithms.

Prerequisites: Operating Systems and Computer Architecture: CSCI-GA 2250 or good understanding of Computer Architecture, C/C++ programming, OS design, process stack/heap, threads, file-system, IO, Networks. Python and C languages: good programming skills in Python and C. Machine Learning and Neural Networks Algorithms: The course is focused on the performance rather than the algorithmic aspects. A basic explanation of the algorithms will be part of the course. However, it is strongly recommended to start the course with a basic understanding of the following algorithms: logistic regression, neural networks, convolutional neural networks.
CSCI-GA.3033-024
(20911) Special Topics: Big Data and ML Systems Lakshminarayanan Subramanian
Office Hours M 7:10-9:00PM CIWW 512
This class aims to cover introductory and recent concepts in big data and machine learning systems. The class will focus on a broad spectrum of big data computational problems, algorithms and platforms. The class will be centered around several big data case studies in commercial use to understand big data and machine learning tasks across different domains and provide an in-depth understanding of the problems and algorithms used in each domain. From the computational platform perspective, this class covers popular and widely used commercial platforms, Spark, streaming platforms, Tensorflow, ML platforms (MLBase, MxNet) and GPU platforms. The goal of this class is to educate students about the algorithms and systems techniques used to build scalable big data and ML platforms and how these platforms are used in the real world.

This class covers the following broad topics:
1. Cloud Infrastructure Blocks: AWS
2. Big Data Algorithms: searching and indexing large data sets, implementing standard statistical algorithms, similar items, nearest neighbors, graph mining, network analytics, dimensionality reduction
3. Big Data Computation Platforms: Spark, TensorFlow, MLBase, MLib, MxNet, GPU computing
4. Scalable ML Algorithms: Implementing deep learning algorithms, graph and network analytics, scalable vision algorithms, scalable NLP algorithms
5. Domain Specific Applications and Case Studies: vision analytics engines, NLP analytics engines, search and graph analytics
CSCI-GA.3033-025
(20912) Special Topics: Cloud and Machine Learning I-Hsin Chung
Office Hours
Seetharami Seelam R 5:10-7:00PM CIWW 201

This is a capstone course.

This course exposes students to various cloud computing models and introduces them to performing machine learning on the cloud. The course material introduces students to various cloud providers including Amazon AWS, Google GCE, and IBM Cloud and their machine learning service capabilities. Students will learn how to build cloud systems for machine learning, the application characteristics, and develop hands-on experience with programming machine learning applications on these cloud platforms.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Basic understudying of operating systems and computer architecture, ability to program in at least one of c/c++, go, or Python languages. Basic understanding of cloud computing systems.
CSCI-GA.3140-001
(20921) Abstract Interpretation Patrick Cousot
Office Hours M 5:10-7:00PM 60 Fifth Ave 446

Many numerical or symbolic problems are very hard or even undecidable meaning that no computer algorithm can solve them exactly in reasonable time and memory space. This course introduces the latest techniques in abstract interpretation, a powerful framework for automatically calculating approximate solutions of such difficult problems. The main emphasis will be on software verification and automatic analysis with a wide range of applications from compilers to social networks.

Prerequisites: Some facility with logic, sets, linear algebra, graphs, formal languages, mathematical proofs, and programming languages is expected.
CSCI-GA.3812-001
(2650) Information Technology Projects * Evan Korth
Office Hours W 6:00-9:00PM CIWW 512

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.

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.
CSCI-GA.3813-001
(3364) Adv Lab MS * STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-002
(2651) Adv Lab MS * STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-003
(2652) Adv Lab MS * STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-004
(2653) Adv Lab MS * STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-005
(2654) Adv Lab PhD ** STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-006
(2655) Adv Lab PhD ** STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-007
(2656) Adv Lab PhD ** STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-008
(2657) Adv Lab PhD ** STAFF -

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3840-001
(2658) Master's Thesis Research * STAFF -

Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
CSCI-GA.3850-001
(25261) PhD Seminar: Cryptography** Yevgeniy Dodis
Office Hours T 12:30-3:15PM CIWW 412
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.

Prerequisites: Permission of the instructor.
CSCI-GA.3850-004
(2659) PhD Seminar: Formal Methods** Thomas Wies
Office Hours R 11:00-12:30PM 60 Fifth Ave 446
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.

Prerequisites: Permission of the instructor.
CSCI-GA.3860-001
(2660) PhD Thesis Research ** STAFF -

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(3358) Internship In Computer Science MS* STAFF -

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.

Prerequisites: Permission of Director of Graduate Studies.
CSCI-GA.3870-002
(3359) Internship in Computer Science PhD* STAFF -

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.

Prerequisites: Permission of Director of Graduate Studies.
MAINT-GA.4747-001 Maintenance of Matriculation (MS Students) STAFF -
Section 1 is for MS students. Section 4 is for non-supported PhD students.
MAINT-GA.4747-004 Maintenance of Matriculation (non-supported PhD students) STAFF -
Section 1 is for MS students. Section 4 is for non-supported PhD students.

*

Indicates controlled enrollment (permission number required for registration). Contact your program advisor.
**

Indicates controlled enrollment (permission number required for registration). Contact Santiago Pizzini (pizzini@cs.nyu.edu).
***

Indicates controlled enrollment (permission number required for registration). Contact the instructor.
****

Indicates controlled enrollment (permission number required for registration). Contact dhss@nyu.edu for assistance.