Course Schedule

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1133-001
(2742) PAC I* Nolan Filter
Office Hours T 6:30-9:00PM CIWW 201

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
(3190) PAC I Recitation Katherine Pully W 7:10-8:00PM CIWW 201

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
(2743) Fundamental Algorithms Yevgeniy Dodis
Office Hours T 5:10-7:00PM 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.1170-002
DS-GA.1170
(2744) Fundamental Algorithms Recitation Harish Karthikeyan R 5:10-6:00PM 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.1170-003
DS-GA.1170
(3514) Fundamental Algorithms Alexander Alekseyev
Office Hours W 5:10-7:00PM CIWW 517

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
(3515) Fundamental Algorithms Recitation Vikas Patidar F 6:10-7:00PM CIWW 517

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
(3752) Mathematical Techniques For CS Applications Remi Imbach
Office Hours M 5:10-7:00PM CIWW 512
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
(2745) Programming Languages Benjamin Goldberg
Office Hours W 5:10-7:00PM 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
(2746) Programming Languages Recitation Goktug Saatcioglu R 6:10-7:00PM 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.2110-003
(3330) Programming Languages Cory Plock
Office Hours M 5:10-7:00PM CIWW 312

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
(3199) Programming Languages Recitation Wai I Ng F 5:10-6:00PM CIWW 312

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
(2747) Scientific Computing Aleksandar Donev
Office Hours R 5:10-7:00PM CIWW 1302
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
(2748) Operating Systems Hubertus Franke
Office Hours M 7:10-9:00PM CIWW 1302
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
(3208) Data Communications and Networks Jean-Claude Franchitti
Office Hours R 7:10-9:00PM CIWW 312

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
(3384) Computer Graphics Kenneth Perlin
Office Hours M 7:10-9:00PM 60 Fifth Ave 110

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
(3331) Computer Vision Rob Fergus
Office Hours R 7:10-9:00PM Silv 405

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
(2749) Numerical Methods I Michael Overton
Office Hours M 5:10-7:00PM CIWW 1302
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
(3205) Database Systems Dennis Shasha
Office Hours M 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.2434-001
(2750) Advanced Database Systems Dennis Shasha
Office Hours T 5:10-7:00PM CIWW 101
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.2565-001
(23839) Machine Learning Rajesh Ranganath
Office Hours T 7:10-9:00PM 60 Fifth Ave 110

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. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics.
CSCI-GA.2566-001
(3332) Foundations Of Machine Learning CANCELLED -

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.2621-001
(20814) Distributed Systems Jinyang Li
Office Hours W 5:10-7:00PM CIWW 101

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
(3749) Foundations of Networks and Mobile Systems* Lakshminarayanan Subramanian
Office Hours T 9:00-10:50AM Tisch 201 Additional information: Open only to students in the MS-CEI 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
(3898) Foundations of Networks and Mobile Systems Lab* Lakshminarayanan Subramanian
Office Hours T 11:00-11:50AM Tisch 201 Additional information: Open only to students in the MS-CEI 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.2820-001
(3750) DevOps and Agile Methodologies John Rofrano
Office Hours W 5:10-7:00PM CIWW 102

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
(3899) DevOps and Agile Methodologies Lab John Rofrano
Office Hours W 7:10-8:00PM CIWW 102

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.2830-001
(3751) The Lean Launch Pad* Andrew Breen
Office Hours R 10:15-12:15PM 16WP 106 Additional information: Open only to students in the MS-CEI program.

This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands on learning on what it’s like to actually start a high tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.
CSCI-GA.2830-002
(3900) The Lean Launch Pad Lab* Emily Baum R 12:25-1:15PM 16WP 106 Additional information: Open only to students in the MS-CEI program.

This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands on learning on what it’s like to actually start a high tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.
CSCI-GA.2945-001
MATH-GA.2011-001 Advanced Topics In Numerical Methods: Monte Carlo Methods Jonathan Weare T 3:20-5:10PM CIWW 1302

This class primarily concerns the design and analysis of Monte Carlo sam-pling techniques for the estimation of averages with respect to high dimen-sional probability distributions. Standard simulation tools such as importance sampling, sequential 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. Particular attention will be paid to the major complicating issues like conditioning and rare events along with methods to address them (e.g. tempering, interacting particle methods, and free-energy methods).
CSCI-GA.2945-002
MATH-GA.2011-002 Advanced Topics In Numerical Methods: Finite Element Methods Georg Stadler
Office Hours W 5:10-7:00PM CIWW 1302

This course covers theoretical and practical aspects of finite element methods for the numerical solution of partial differential equations. The first part of the course will focus on theoretical foundations of the method (calculus of variations, Poincare inequality, Cea's lemma, Nitsche trick, convergence estimates). The second part targets practical aspects of the method, illustrates how it can be implemented and used for solving partial differential equations in two and three dimensions. Examples will include the Poisson equation, linear elasticity and, time permitting, the Stokes equations.

Prerequisites: Prerequisites are a graduate PDE course, Numerical Methods II (or equivalent) and some programming experience.
CSCI-GA.3033-001
(2753) Special Topics: Production Quality Software Michael Schidlowsky
Office Hours W 5:10-7:00PM CIWW 317

In this course we study the craft of software engineering holistically. The semester begins with a thorough review of Java and the Java Virtual Machine. We concentrate on library components and language features that are most commonly used. Later lectures cover more advanced topics including concurrency and thread-safety, object-oriented programming, design patterns, and testing. The course also covers non-technical topics critical to software development like developing and reinforcing salutary engineering habits, communicating effectively, and addressing issues of productivity and drive in engineering teams. Lessons are reinforced with bi-weekly assignments that are carefully graded with the aim of providing as much critical feedback as possible. Some student submissions are critiqued in classroom code reviews.
CSCI-GA.3033-002
(2754) 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 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.3033-004
(2755) 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.3033-005
(25093) Special Topics: Big Data Application Development Suzanne McIntosh
Office Hours T 7:10-9:00PM CIWW 317

This is an introductory course on Spark programming, Spark architecture, Spark SQL, Spark Streaming, and integration of Spark with the Hadoop ecosystem for developing Big Data analytics applications. The course project can be completed with Scala or Python, and Spark. This course covers technologies that integrate well with Spark in the creation of Big Data analytics applications.

Students are required to complete weekly reading and programming assignments and demonstrate mastery of course topics by developing a final project using Spark.

Prerequisites: Prerequisites include experience with Hadoop, which commonly comes from having completed the Realtime and Big Data Analytics course. There will be a review of Apache Hadoop, but students should have experience with Hadoop MapReduce and HDFS.
CSCI-GA.3033-006
(2756) Special Topics: Realtime and Big Data Analytics Suzanne McIntosh
Office Hours R 7:10-9:00PM CIWW 102

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.3033-008
(3386) Special Topics: Statistical Natural Language Processing Ankur Parikh
Office Hours T 5:10-7:00PM CIWW 512

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.3033-011
(3446) Special Topics: Predictive Analytics Anasse Bari
Office Hours W 5:10-7:00PM 60 Fifth Ave C15

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.3033-012
(3444) Special Topics: Vision Meets Machine Learning Davi Geiger
Office Hours R 5:10-7:00PM CIWW 102

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.3033-017
(3525) Special Topics: Multicore Programming CANCELLED -

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-020
(3551) Special Topics: High Performance Machine Learning Ulrich Finkler
Office Hours
Wei Zhang W 7:10-9:00PM CIWW 317

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.3033-022
MATH-GA 2830-001
(3922) Special Topics: Quantum Computation Oded Regev
Office Hours M 5:10-7:00PM CIWW 517

Quantum computers perform computation using quantum phenomena, and are remarkably efficient in certain tasks such as factoring integer numbers. This course will be an introduction to this area. Topics include basic quantum algorithms like Deutsch-Jozsa, Simon, and Grover, as well as Shor's celebrated factoring algorithm. We will also talk about some basic and surprising quantum information concepts like quantum entanglement, teleportation, and superdense coding, as well as the Einstein–Podolsky–Rosen paradox. The course will be of interest to students working in computer science, mathematics, or physics.

Prerequisites: A strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), and some familiarity with discrete probability (e.g., NYU’s MATH-UA 235 Probability and Statistics). Familiarity with theory of computation (e.g., NYU’s CSCI-UA.0310 Basic Algorithms) would also be useful. No background in physics is required.
CSCI-GA.3033-027
(26031) Special Topics: Bayesian Machine Learning Andrew Gordon Wilson
Office Hours T 5:10-7:00PM CIWW 102

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.
CSCI-GA.3110-001
(3612) Honors Programming Languages CANCELLED -
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
(3754) Applied Cryptography & Network Security Constantine Christakos
Office Hours T 7:10-9:00PM CIWW 312
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
(2757) Honors Analysis of Algorithms Subhash Khot
Office Hours TR 9:30-10:45AM CIWW 101
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
(2758) 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
(2759) 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
(2760) 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
(2761) 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
(2762) 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
(2763) 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
(2764) 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
(2765) 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
(2766) 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
(2767) 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
(21583) 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
(2768) 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
(2769) PhD Thesis Research ** STAFF -

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(3489) 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
(3490) 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.