Course Schedule

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1133-001
(2994) PAC I* Evan Korth
Office Hours T 6:30-9:00PM ONLINE

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.1133-002
(3424) PAC I Recitation Nolan Filter W 6:10-7:00PM ONLINE

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.1133-003
(25317) PAC I Recitation CANCELLED -

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.1170-001
DS-GA.1170
(2995) Fundamental Algorithms Richard Cole
Office Hours T 5:10-7:00PM ONLINE

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
(2996) Fundamental Algorithms Recitation Harish Karthikeyan R 5:10-6:00PM CIWW 109 Additional information: Recitation for Section 001

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
(3690) Fundamental Algorithms Alexander Alekseyev
Office Hours W 5:10-7:00PM ONLINE

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
(3691) Fundamental Algorithms Recitation Agnitha Mohan Ram F 6:10-7:00PM ONLINE Additional information: Recitation for Section 003

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.1170-005
DS-GA.1170
(25318) Fundamental Algorithms Recitation Srikar Varadaraj R 5:10-6:00PM ONLINE Additional information: Recitation for Section 001

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.1170-006
DS-GA.1170
(25319) Fundamental Algorithms Recitation Fengyuan Liu F 7:10-8:00PM ONLINE Additional information: Recitation for Section 001

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.1170-007
DS-GA.1170
(25320) Fundamental Algorithms Recitation CANCELLED - Additional information: Recitation for Section 001

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.1170-011
DS-GA.1170
(26502) Fundamental Algorithms Chee Yap
Office Hours M 5:45-7:35PM 1555 Century 201 Additional information: Open only to NYU Shanghai GoLocal students

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.1170-012
DS-GA.1170
(26503) Fundamental Algorithms Recitation Chee Yap
Office Hours T 5:10-6:00PM 1555 Century 1505 Additional information: Open only to NYU Shanghai GoLocal students

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
(3830) Mathematical Techniques For CS Applications Ernest Davis
Office Hours M 5:10-7:00PM ONLINE
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
(2997) Programming Languages Benjamin Goldberg
Office Hours W 5:10-7:00PM ONLINE

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
(2998) Programming Languages Recitation Aishwarya Rajan
Ketan Kanishka R 6:10-7:00PM CIWW 109 Additional information: Recitation for Section 001

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.2110-003
(3548) Programming Languages Cory Plock
Office Hours M 5:10-7:00PM ONLINE

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.2110-004
(3433) Programming Languages Recitation CANCELLED - Additional information: Recitation for Section 003

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.2110-005
(25321) Programming Languages Recitation CANCELLED - Additional information: Recitation for Section 001

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.2110-006
(25322) Programming Languages Recitation Aishwarya Rajan
Ketan Kanishka F 8:10-9:00PM CIWW 109 Additional information: Recitation for Section 001

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.2110-007
(25323) Programming Languages Recitation Monika Dagar R 11:00-11:50AM CIWW 109 Additional information: Recitation for Section 003

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.2110-011
(26504) Programming Languages Yuxin Deng
Jing Liu W 5:10-7:00PM 16 Qi Xia 917

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.2110-012
(26506) Programming Languages Recitation Yuxin Deng
Jing Liu T 6:30-7:20PM 16 Qi Xia 917 Additional information: Open only to NYU Shanghai GoLocal students

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.2112-001
MATH-GA 2043-001
(2999) Scientific Computing Aleksandar Donev R 5:10-7:00PM ONLINE
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.

Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
CSCI-GA.2250-001
(3000) Operating Systems Hubertus Franke
Office Hours M 7:10-9:00PM ONLINE
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
(3442) Data Communications and Networks Jean-Claude Franchitti
Office Hours R 7:10-9:00PM ONLINE

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.2270-001
(3593) Computer Graphics Daniele Panozzo
Office Hours M 7:10-9:00PM ONLINE

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.

Prerequisites: CSCI-GA 1170 and CSCI-UA 140 (or an equivalent undergraduate course in linear algebra).
CSCI-GA.2271-001
(3549) Computer Vision Rob Fergus
Office Hours R 7:10-9:00PM ONLINE

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.

Prerequisites: CSCI-GA 1170
CSCI-GA.2420-001
MATH-GA 2010-001
(3001) Numerical Methods I Michael Overton
Office Hours M 5:10-7:00PM ONLINE
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
(3439) Database Systems Zvi Kedem
Office Hours M 5:10-7:00PM ONLINE
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.2433-011
DS-GA.2433-001
(26507) Database Systems Xiaoyang Sean Wang R 3:15-5:05PM 1555 Century 210 Additional information: Open only to NYU Shanghai GoLocal students
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.2434-001
(3002) Advanced Database Systems Dennis Shasha
Office Hours T 5:10-7:00PM CIWW 109
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.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2436-001
(22054) Realtime and Big Data Analytics Lakshminarayanan Subramanian
Office Hours R 7:10-9:00PM ONLINE

This course introduces architectures and technologies at the foundation of the Big Data movement. These technologies facilitate scalable management and processing of vast quantities of data collected through realtime and near realtime sensing. We explore tools enabling the acquisition of data in the social domain and the fusion of those data when in flight and at rest using Hadoop and Hadoop-related tools.

The material covered in this course aligns with the prevailing state of the art in Big Data technologies, which continues to be a rapidly evolving landscape as new technologies emerge and existing ones evolve and mature.

Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by designing, developing, and demonstrating an analytics project of their choosing. Class time will be set aside for project proposal and final demo.

This is an introductory Hadoop course intended for students who have no experience with the Apache Hadoop ecosystem of tools and frameworks.
CSCI-GA.2566-001
(19796) Foundations Of Machine Learning* Mehryar Mohri
Office Hours T 5:10-7:00PM ONLINE

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).

Prerequisites: CSCI-GA 1180.
CSCI-GA.2590-001
(22903) Natural Language Processing He He
Office Hours T 7:10-9:00PM ONLINE

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.2621-001
(4063) Distributed Systems Aurojit Panda
Office Hours W 5:10-7:00PM ONLINE

Large-scale distributed systems lie at the core of application domains such as cloud computing, internet of things, large multiplayer games, etc. These application domains make use of systems such as distributed databases (e.g., Google's Spanner, Amazon's Dynamo, etc.), large scale analytics frameworks (e.g., Hadoop, Spark and TensorFlow), distributed locking systems (e.g., Paxos, etcd, etc.), and others. In this class we will look at how to construct these distributed systems, in particular looking at why this is more complex than building applications running on a single machine, and present abstraction and design techniques for building distributed systems. We will focus on a solving a variety of common problems in these systems including consensus, consistency, naming, fault tolerance, etc.

This is a graduate-level course, but undergraduates are welcome! The course itself will consist of a series of lectures and will require reading research papers.

This class satisfies the Ph.D. breadth requirement in Systems and also serves as a M.S. capstone course.

Prerequisites: 1. Undergraduate Operating Systems or Computer Systems Organization; 2. programming experience; 3. mathematical maturity or the willingness to work through simple proofs.
CSCI-GA.2630-001
(3827) Foundations of Networks and Mobile Systems* CANCELLED - Additional information: Open only to students in the MS-CEI program

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Webbased systems, providing an introduction to datacenter networks, clouds, middle boxes (proxies, firewalls) and next generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.
CSCI-GA.2630-002
(3889) Foundations of Networks and Mobile Systems Lab* CANCELLED - Additional information: Open only to students in the MS-CEI program.

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Webbased systems, providing an introduction to datacenter networks, clouds, middle boxes (proxies, firewalls) and next generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.
CSCI-GA.2810-001
(19797) Design and Innovation* CANCELLED - Additional information: Open only to students in the MS-CEI program.

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

Prerequisites: TBA
CSCI-GA.2810-002
(22060) Design and Innovation Lab* CANCELLED - Additional information: Open only to students in the MS-CEI program.

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

Prerequisites: TBA
CSCI-GA.2820-001
(3828) DevOps and Agile Methodologies John Rofrano W 5:10-7:00PM ONLINE

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
(3890) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM ONLINE

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.2945-001
MATH-GA 2011-001 Advanced Topics In Numerical Methods: Monte Carlo Methods Jonathan Goodman
Office Hours W 5:10-7:00PM CIWW 1302
Topics vary each semester.

Prerequisites: Topics determine prerequisites.
CSCI-GA.2945-002
MATH-GA 2011-002 Advanced Topics In Numerical Analysis: Numerical Optimization CANCELLED -
Topics vary each semester.

Prerequisites: Topics determine prerequisites.
CSCI-GA.2965-001
(22062) Heuristic Problem Solving Dennis Shasha
Office Hours M 5:10-7:00PM CIWW 109
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.

Prerequisites: CSCI-GA 1170 and an ability to prototype algorithms rapidly.
CSCI-GA.3033-025
(3005) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Mohamed Zahran
Office Hours W 5:10-7:00PM CIWW 201

This course serves as a capstone for the MSCS program.

In this course, we will cover architectural aspects and capabilities of modern GPUs (graphics processing unit) and will learn the architecture and the programming of GPUs to solve different type of problems. Many computations can be performed much faster on the GPU than on a traditional processor. This is why GPUs are present now in almost all computers; and the majority of Top 500 supercomputers in the world are built around GPUs. GPUs are no longer restricted to graphics applications but are now used for a diverse set of applications and domains. This introduces the concept of general-purpose GPUs or GPGPUs, which is the main subject of this course.
CSCI-GA.3033-046
(3595) Special Topics: Statistical Natural Language Processing CANCELLED -

In this course we will examine some of the core tasks in natural language processing, starting with simple word-based models for text classification and building up to more complex models for syntactic parsing, machine translation, question answering, and text generation. The course will cover a variety of statistical and machine learning approaches including recent advances in deep learning. In addition to the core topics, we will discuss recent research progress in the area such as unsupervised pretraining and adversarial examples. This course assumes a good background in basic probability as well as interest in building real systems.
CSCI-GA.3033-061
(3641) Special Topics: Predictive Analytics Anasse Bari
Office Hours W 5:10-7:00PM CIWW 109

Predictive analytics is the art and science of extracting useful information from historical data and present data for the purpose of predicting future trends. In this course, students will have an introduction to the phases of the analytics lifecycle and a basic understanding of a variety of tools and machine learning techniques to analyze data to discover forward insights. Several techniques will be introduced; including data pre-processing techniques, data clustering algorithms, data classification algorithms, association rules data mining algorithms, recommender systems, etc.

Applications from bio-informatics, social networks analytics, and text mining will be covered. Highlights from industrial use cases will be covered to demonstrate how Predictive Analytics relates to improving business performance and impacting better decisions. This is an introductory course that will provide students with basic skills of the new generation of data scientists that will allow them to structure, analyze and derive useful insights from data that could help make better decisions.
CSCI-GA.3033-076
(3640) Special Topics: Vision Meets Machine Learning CANCELLED -

This course will introduce basic ideas of current Machine Learning methods (Deep Learning with Variational Auto Encoders, GAN, RNN, Reinforcement Learning). The course is "hands on", with small groups programming/developing projects that integrate the basic ideas above to perform "computer vision tasks" (the evaluation is based on projects). The vision tasks will be in Object Classification, Human Detection, and Human Actions.

Prerequisites: Students who take this class should have familiarity with linear algebra, probability, and calculus as well as programming in python.
CSCI-GA.3033-083
DS-GA.3001-005
(23046) Special Topics in Data Science: Machine Learning for Healthcare Rajesh Ranganath
Office Hours M 2:00-3:40PM HEBU CR1

The course covers machine learning methods important for healthcare including causal inference, k-shot learning, time series models, and fairness. We will learn about important clinical questions and the data (such as from ICU monitors and imaging) that along with machine learning can help answer these questions. We will focus on recent work in machine learning for healthcare.

Students must enroll into Lab section 091 with Lecture section 083.
CSCI-GA.3033-084
(3716) Special Topics: High Performance Machine Learning Ulrich Finkler
Office Hours W 7:10-9:00PM ONLINE

This is a capstone course for the MSCS program.

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.3033-085
(3004) Special Topics: Cloud and Machine Learning I-Hsin Chung
Office Hours
Seetharami Seelam R 5:10-7:00PM ONLINE

This is a capstone course for the MSCS program.

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.3033-087
DS-GA.3001-011
(4232) Special Topics in Data Science: Bayesian Machine Learning Andrew Gordon Wilson
Office Hours W 5:00-6:40PM HEBU CR1

This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will be comprised of three units, alongside a major research project component. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization. 3. Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference. Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning (2006) before class starts. Prior courses on machine learning are strongly recommended.

Students must enroll into Lab section 092 with Lecture section 087.
CSCI-GA.3033-090
(3003) Special Topics: Deep Reinforcement Learning* Lerrel Pinto
Office Hours F 5:10-7:00PM CIWW 317

This course is taught at a PhD-level.

Deep Reinforcement Learning (RL) has made massive strides in the last decade for sequential decision making problems. This course serves as a graduate-level introduction to RL, with an emphasis on applications and recent research. Key topics will include: Basic formalisms of RL; Exploration vs exploitation; Imitation learning; Model-free RL; Model-based control and planning; Unsupervised learning for RL; Applications to games, robotics, industry; Current frontiers. This course will involve several coding home-works where you will implement various algorithms, and a final project. Alternative titles for this course: Adaptive control and learning, Dynamic optimization.

Prerequisites: Graduate level knowledge of Machine Learning, Computer Vision, and Deep Learning. Proficiency in Python to complete the home-works.
CSCI-GA.3033-091
DS-GA.3001-006
(23047) Special Topics in Data Science: Machine Learning for Healthcare Lab Aalad Manas Puli T 1:00-1:50PM Silv 405 Additional information: Lab for CSCI-GA.3033-083

The course covers machine learning methods important for healthcare including causal inference, k-shot learning, time series models, and fairness. We will learn about important clinical questions and the data (such as from ICU monitors and imaging) that along with machine learning can help answer these questions. We will focus on recent work in machine learning for healthcare.

Students must enroll into Lecture section 083 with Lab section 091.
CSCI-GA.3033-092
DS-GA.3001-012
(23201) Special Topics in Data Science: Bayesian Machine Learning Lab Pavel Izmailov
Zijian Liu M 7:00-7:50PM Silv 405 Additional information: Lab for CSCI-GA.3033-087

This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will be comprised of three units, alongside a major research project component. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization. 3. Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference. Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning (2006) before class starts. Prior courses on machine learning are strongly recommended.

Students must enroll into Lecture section 087 with Lab section 092.
CSCI-GA.3033-093
(26509) Special Topics: Introduction to Deep Learning Systems Parijat Dube
Office Hours T 7:10-9:00PM CIWW 101 Additional information: Syllabus for CSCI-GA.3033-093

The course will cover several topics related to Deep Learning (DL) systems and their performance.

Both algorithmic and system related building blocks of DL systems will be covered including DL training algorithms, network architectures, and best practices for performance optimization. We will study hyperparameter choices, scalable distributed DL training, Kubernetes based DL system stack on cloud, tools and benchmarks for performance evaluation of DL systems, transfer learning etc. Emphasis will be on getting working knowledge of tools and techniques for performance evaluation of DL systems. The students will gain practical experience working on different stages of DL life cycle, including model development, testing, and deployment. The assignments will be mostly hands-on involving standard DL frameworks (Tensorflow, Pytorch) and open source technologies.

Prerequisites: An introductory machine learning course. Knowledge of python is preferred but not necessary.
CSCI-GA.3110-001
(22063) Honors Programming Languages Patrick Cousot TR 3:30-4:45PM ONLINE
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.

Prerequisites: Permission of the instructor for master’s students
CSCI-GA.3205-001
(3831) Applied Cryptography & Network Security Zvi Kedem
Office Hours T 7:10-9:00PM ONLINE
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.3520-001
(3007) Honors Analysis of Algorithms Subhash Khot TR 9:30-10:45AM 60 Fifth Ave C15
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.

Prerequisites: Permission of the instructor for master’s students.
CSCI-GA.3812-001
(3008) Information Technology Projects * Jean-Claude Franchitti
Office Hours M 6:10-9:10PM CIWW 317

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
(3009) 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
(3010) 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
(3011) 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
(3012) 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
(3013) 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
(3014) 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
(3015) 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
(3016) 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
(3017) 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
(4113) PhD Seminar: Cryptography ** Yevgeniy Dodis T 12:30-3:15PM ONLINE
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
(3018) PhD Seminar: Formal Methods** Thomas Wies R 11:00-12:30PM ONLINE
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
(3019) PhD Thesis Research ** STAFF -

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(3671) 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
(3672) 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
(3157) Maintenance of Matriculation (MS Students) STAFF -
Section 1 is for MS students. Section 4 is for non-supported PhD students.
MAINT-GA.4747-004
(3160) 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.