Course Schedule

Current semester Spring 2025, and future semesters are now available to view.

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-001
DS-GA.1170-001
(5115) Fundamental Algorithms Chee Yap W 4:55-6:55PM 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.1170-002
(5116) Fundamental Algorithms Recitation Bingwei Zhang R 5:55-6:45PM Meyr 122 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
(5117) Fundamental Algorithms Recitation Bingwei Zhang F 4:55-5:45PM 60 Fifth Ave 150 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.1170-004
(5118) Fundamental Algorithms Yevgeniy Dodis T 4:55-6:55PM CIWW 312

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
(5119) Fundamental Algorithms Recitation Eli Goldin R 3:45-4:35PM 60 Fifth Ave 110 Additional information: Recitation for Section 004

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
(5123) Mathematical Techniques For CS Applications Parijat Dube R 7:10-9:10PM CIWW 101

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
(5124) Programming Languages Cory Plock M 4:55-6:55PM 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
(5125) Programming Languages Recitation Anway Agte R 7:10-8:00PM Silv 206 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
(5126) Programming Languages Recitation Hrithik Dhoka F 11:15-12:05PM Meyr 122 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.2110-004
(5127) Programming Languages CANCELLED -

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
(5128) Programming Languages Recitation CANCELLED - Additional information: Recitation for Section 004

Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2130-001
(5132) Compiler Construction Joseph Tassarotti M 7:10-9:10PM CIWW 312
This is a capstone course based on compilers and modern programming languages. The topics covered include structure of one-pass and multiple-pass compilers; symbol table management; lexical analysis; traditional and automated parsing techniques, including recursive descent and LR parsing; syntax-directed translation and semantic analysis; run-time storage management; intermediate code generation; introduction to optimization; and code generation. The course includes a special compiler-related capstone project, which ties together concepts of algorithms, theory (formal languages), programming languages, software engineering, computer architecture, and other subjects covered in the MS curriculum. This project requires a substantial semester-long programming effort, such as construction of a language compilation or translation system that includes lexical and syntactic analyzers, a type checker, and a code generator.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2250-001
(5133) Operating Systems Hubertus Franke M 7:10-9:10PM CIWW 109

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2250-002
(5134) Operating Systems Yang Tang T 4:55-6:55PM CIWW 101

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2262-001
(5135) Data Communications and Networks Jean-Claude Franchitti M 7:10-9:10PM 60 Fifth Ave 150

This course teaches the design and implementation techniques essential for engineering robust networks. Topics include networking principles, Transmission Control Protocol/Internet Protocol, naming and addressing (Domain Name System), data encoding/decoding techniques, link layer protocols, routing protocols, transport layer services, congestion control, quality of service, network services, programmable routers and overlay networks.

Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
CSCI-GA.2421-001
MATH-GA 2020-001
(7892) Numerical Methods II Michael O'Neil MW 11:00-12:15PM CIWW 1302

This course will cover fundamental methods that are essential for the numerical solution of differential equations. It is intended for students familiar with ODE and PDE and interested in numerical computing; computer programming assignments form an essential part of the course. The course will introduce students to numerical methods for (1) nonlinear equations, Newton's method; (2) ordinary differential equations, Runge-Kutta and multistep methods, convergence and stability; (3) finite difference, finite element and integral equation methods for elliptic partial differential equations; (4) fast solvers, multigrid methods; and (5) parabolic and hyperbolic partial differential equations.

Text: LeVeque, R. (2007). Classics in Applied Mathematics [Series]. Finite Difference Methods for Ordinary and Partial Differential Equations. Philadelphia, PA: Society for Industrial and Applied Mathematics

Prerequisites: Numerical linear algebra, elements of ODE and PDE.
CSCI-GA.2433-001
DS-GA.2433-001
(5136) Database Systems Matthew Zeidenberg W 4:55-6:55PM 60 Fifth Ave 150
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.2436-001
(5185) Realtime and Big Data Analytics Yang Tang R 4:55-6:55PM CIWW 101

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.2437-001
(5186) Big Data Application Development Yang Tang T 7:10-9:10PM CIWW 101

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.2565-001
(5137) Machine Learning Matus Telgarsky W 7:10-9:10PM CIWW 109

This course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. The course covers the mathematical methods and theoretical aspects but primarily focuses on algorithmic and practical issues.

Prerequisites: Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics.
CSCI-GA.2569-001
DS-GA.1005-001
(5484) Inference and Representation Joan Bruna M 2:00-3:40PM 60 Fifth Ave 150

This course covers graphical models, causal inference, and advanced topics in statistical machine learning.

Prerequisites: DS-GA 1004
CSCI-GA.2569-002
DS-GA.1005-002
(5485) Inference and Representation Lab STAFF T 1:30-2:20PM Silv 520 Additional information: Lab for Section 001

This course covers graphical models, causal inference, and advanced topics in statistical machine learning.

Prerequisites: DS-GA 1004
CSCI-GA.2572-001
DS-GA.1008-001
(5482) Deep Learning Alfredo Canziani M 4:55-6:35PM Silv 207

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2572-002
DS-GA.1008-002
(5483) Deep Learning Lab TBA W 3:45-4:35PM 12WP G08 Additional information: Lab for Section 001

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2590-001
BMIN-GA 2590-001
(5138) Natural Language Processing He He W 4:55-6:55PM Silv 408

Natural Language Processing applies computational and linguistic knowledge to the processing of natural languages (English, Chinese, Spanish, Japanese). Applications include: machine translation, information extraction, information retrieval, and others. On the one hand, the class will include the modeling and representation of linguistic phenomena. On the other, It will cover methods for applying this knowledge using both manual rules and machine learning. Sample topics include: formal languages, hmm, part of speech tagging, vector-based methods, shallow and full parsing, semantic role labeling, information extraction and machine translation. Students will complete shared tasks (POS-tagging, Information Extraction, etc.) and group final projects. There will also be a midterm exam and several small written assignments.

Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.

Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.
CSCI-GA.2620-001
(5139) Networks and Mobile Systems Anirudh Sivaraman W 10:15-12:15PM GCASL 361

This is a capstone course. A course in computer networks and large-scale distributed systems. Teaches the design and implementation techniques essential for engineering both robust networks and Internet-scale distributed systems. The goal is to guide students so they can initiate and critique research ideas in networks and distributed systems and implement and evaluate a working system that can handle a real-world workload. Topics include routing protocols, network congestion control, wireless networking, peer-to-peer systems, overlay networks and applications, distributed storage systems, and network security.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2621-001
(5183) Distributed Systems Aurojit Panda W 4:55-6:55PM 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.2820-001
(5172) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program

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
(5173) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Additional information: Open only to students in the Stern TECH MBA program

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.2820-003
(5174) DevOps and Agile Methodologies John Rofrano W 4:55-6:55PM 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.2820-004
(5175) 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.2830-001
(5176) The Lean Launch Pad* Yiannis Broustas R 10:15-12:15PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program

Only open to MS students in the Computing, Entrepreneurship, and Innovation 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
(5177) The Lean Launch Pad Lab* Yiannis Broustas R 12:30-1:20PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program

Only open to MS students in the Computing, Entrepreneurship, and Innovation 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.3033-026
(5155) Special Topics: Cloud Computing Jean-Claude Franchitti R 7:10-9:10PM CIWW 312

This course is a capstone course that introduces the latest cloud multi-access computing platform services including support for Internet of Things (IoT), Artificial Intelligence (AI), Web3 decentralized platforms, and other technologies. These services may be leveraged to create industry 4.0 and 5.0 innovative Intelligent Autonomous Networked (IAN) business solutions and deploy them on hybrid multi-cloud platforms so they can operate at the network edge to minimize latency and power consumption. Students will learn about services provided on the top “Big Clouds”, namely Amazon AWS Cloud, Google Cloud, Microsoft Azure Cloud, IBM Cloud, Oracle Cloud, Salesforce, and others. Related services provided on the Cloud and covered in the course include computing and hosting services, storage services, networking services, container management services (e.g., K8s), and shallow/deep machine learning services. Once familiar with Cloud services, Students will learn how to combine them to support cognitive computing and enable the creation of IAN solutions that cater to the practical needs of industry 4.0/5.0 and society 5.0 users in novel and creative ways. As they create these IAN solutions, students will learn about immersive Augmented/Virtual Reality (AR/VR) User Experience and Interfaces (UX/UI), hybrid decentralized computing and DApps, multi-access computing and Internet of Things (IoT), Mobility/5G networking and Software Defined Network (SDN) infrastructures, advanced robotics, and various key aspects of digital security that are critical to ensure the safety of modern software solutions. Throughout the course, students will develop their own Intelligent Autonomous Networked (IAN) business solutions using Cloud-based shallow/deep machine learning and other services and capabilities as noted above. Sample platforms and related application components will be provided as applicable to speed up the development and deployment process.

Prerequisites: Programming experience in C/C++, JavaScript, Python, and SQL; basic understanding of assembly language; working knowledge of web application development.
CSCI-GA.3033-034
(19062) Special Topics: Multicore Processors: Architecture & Programming Mohamed Zahran W 12:30-2:30PM Online

This course serves as a capstone for the MSCS program.

The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable.
CSCI-GA.3033-052
(5157) Special Topics: Advanced Machine Learning Mehryar Mohri T 4:55-6:55PM CIWW 317

This course discusses advanced topics in theoretical machine learning, extending beyond the scope of foundational graduate courses. The primary goal is to introduce key concepts not covered in basic ML courses such as Foundations of Machine Learning, while also exploring cutting-edge learning problems that can serve as a springboard for research or the development of novel application-relevant techniques.

A central focus of the course is a rigorous analysis of the rich field of online learning. In addition, the material encompasses a broad range of advanced topics in supervised learning, providing a comprehensive overview of the theoretical underpinnings of modern machine learning methods.

Through a combination of lectures, discussions, and assignments, students will gain a deep understanding of the fundamental principles governing learning algorithms. The course will equip students with the necessary theoretical foundations to conduct cutting-edge research in theoretical machine learning, as well as to develop novel and effective machine learning solutions for real-world problems.
CSCI-GA.3033-059
(5158) Special Topics: Big Data Science Anasse Bari W 4:55-6:55PM CIWW 317

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

Prerequisites: Strong programming skills in the Java language and fundamentals of linear algebra.
CSCI-GA.3033-071
(5159) Special Topics: Geometric Modeling Daniele Panozzo M 4:55-6:55PM 60 Fifth Ave C10

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

Prerequisites: Experience with python programming, CSCI-UA 140 (or an equivalent undergraduate course in linear algebra). Some background in computer graphics is helpful, but not necessary.
CSCI-GA.3033-076
(5160) Special Topics: Vision Meets Machine Learning Davi Geiger W 10:15-12:15PM 60 Fifth Ave C15

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.3033-077
(5161) Special Topics: Big Data and ML Systems Lakshminarayanan Subramanian T 10:15-12:15PM 60 Fifth Ave C15

This class will discuss recent research on machine learning systems, esp. those targeted at accelerating deep learning workloads. We will take a deep dive exploring how these systems work so that ML models can be written in a high-level language and executed as low-level kernels on parallel hardware accelerators. Topics covered in this course include: basics of neural networks, how they are programmed and executed by today's deep learning frameworks, automatic differentiation, deep learning accelerators, distributed training techniques, computation graph optimizations, automated kernel generation.

The class will require reading research papers, presenting papers, working on programming labs, and completing a significant final project.

This is a capstone course for the MSCS program.

Prerequisites: The class does require an undergraduate-level knowledge in operating systems, programming languages, algorithms, linear algebra, probability and statistics. If you are new to these concepts, then this class may be advanced for you.
CSCI-GA.3033-085
(5162) Special Topics: Cloud and Machine Learning Hao Yu R 4:55-6:55PM CIWW 317

This is a capstone course for the MSCS program. This course exposes students to the practical aspects of the state of art in cloud computing with an emphasis on employing cloud for machine learning problems. The course material introduces students to various cloud technologies such as virtualization and container. Students will learn how to build machine learning systems using cloud computing, the application performance characteristics, and develop hands-on experience with programming machine learning applications on the cloud platforms.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Python programming. Students are expected to know basic Linux commands and configurations. Software communication and development tools such as Slack and Github will be used.
CSCI-GA.3033-090
(5170) Special Topics: Deep Decision Making & Reinforcement Learning Lerrel Pinto F 4:55-6:55PM 60 Fifth Ave C15

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.3033-097
(5164) Special Topics: Virtual Reality* Kenneth Perlin T 4:55-6:55PM 60 Fifth Ave 446

Students will learn how to create shared interactive experiences in Virtual Reality (VR), using an open-source WebXR platform. This is a hands-on projects based course. Topics include design and implementation of interactive tools for collaborative modeling and animation, with a focus on graphical user interface (GUI) design for VR.

Apply for a seat in the course: https://forms.gle/ak6XsSX32XvkszsW8

Prerequisites: CSCI-GA.2270 Computer Graphics or CSCI-UA.0480-041 Computer Graphics. Permission of the instructor required.
CSCI-GA.3033-099
DS-GA 3001-009
(5469) Special Topics in DS: Introduction to Computer Vision Jean Ponce M 4:55-6:35PM 60 Fifth Ave 150

This course aims to cover broad topics in computer vision, and is not primarily a deep learning course. We will cover topics in traditional computer vision such as camera geometry, image formation, segmentation, object recognition, classification, and detection.
CSCI-GA.3033-100
(5165) Special Topics: Public Interest Technology CANCELLED -

Technologists are in a unique position to utilize their knowledge and skills in the public interest. This course is an introduction to the theory and practice of public interest technology, with substantial projects that will give students the opportunity to gain experience working with external stakeholders on real public-interest issues. For example, students will have the opportunity to work on a mission-driven advocacy project on a current tech policy issue in the US, e.g., related to surveillance, privacy, or AI. Collaborating organizations that students may get to work with include the ACLU, the Policing Project at NYU Law School, the Knight First Amendment Institute at Columbia University, the Surveillance Technology Oversight Project (STOP), and more.

The aim of this course is to elaborate strategies of engagement with broader multidisciplinary social issues related to technology and the public interest. Thus the course consists of two parts: (1) a regular class meeting covering theories, practical approaches, and case studies of public interest technology, and (2) a project-based practicum in which students will work in groups with partners (individuals or organizations) to address a specific public interest issue. This course has no exam; grading will be based on project work and class participation. A majority of students' time will be spent collaboratively in pairs/groups for the project practicum, with weekly instructor-team meetings. In-person class attendance is required, due to the project-based and collaborative nature of the course.

Admission to this course is by application only, via this form: https://docs.google.com/forms/d/1tH7jO1hyULRsF0Mdaa2dFUTVrfxoXhWTzgq_AneFNOg/edit Applications will be evaluated on a rolling basis starting November 11, 2024.
CSCI-GA.3033-102
(5166) Special Topics: Learning with Large Language and Vision Models Saining Xie M 4:55-6:55PM CIWW 101

This course delves into the recent advancements in deep learning made possible by the development of large pre-trained language and vision models. These models have revolutionized the field, demonstrating remarkable capabilities in generation and comprehension, and forming the foundation of cutting-edge AI systems across a wide range of applications. The research-oriented course encompasses a variety of topics in language, vision, and multi-modal modeling. The course structure includes a combination of lectures, paper review sessions, and hands-on projects throughout the semester.

Prerequisites: Students must have completed courses in either deep learning, machine learning, or computer vision and demonstrate proficiency in Python programming.
CSCI-GA.3033-106
Math-GA.2840-003
(5167) Special Topics: Geometric Methods in Algorithm Design Subhash Khot T 10:15-12:15PM CIWW 202

For many NP-hard problems, one can efficiently compute approximate solutions with a non-trivial guarantee on the approximation ratio. Starting with the seminal work of Goemans and Williamson in early 90s, several approximation algorithms have been designed based on a semi-definite programming relaxation followed by a rounding scheme. This course will cover many of these SDP-based algorithms along with other applications of SDPs in computer science. The course will also cover hardness of approximation results, showing that for several NP-hard problems, it remains NP-hard to compute approximations beyond a certain threshold.
CSCI-GA.3033-107
(5168) Special Topics: Cryptography of Blockchains Benedikt Bünz M 12:30-2:30PM 60 Fifth Ave C15

Blockchains are distributed systems that are secured using consensus protocols, game theoretic incentives and cryptographic primitives. This course is a deep dive into the different cryptography that is used in blockchains. We will dive into different basic and advanced cryptographic primitives such as signatures, accumulators and merkle trees, threshold cryptography, zero-knowledge proofs, and others and explore how they are applied in the blockchain field.

Prerequisites: Strong mathematical background, Introductory cryptography (CSCI-GA.3210-001) or similar. No knowledge of blockchains is required. The course is mostly self-contained. Contact instructor if unsure.
CSCI-GA.3033-109
(5169) Special Topics: Computer Vision for Science and Engineering David Fouhey T 4:55-6:55PM CIWW 202

One of the most important emerging areas for computer vision and machine learning is in helping the sciences and engineering extract more and better information from their data. This course will explore the applications of computer vision in the sciences and engineering, including some of the challenges that are not normally seen in "standard" machine learning and vision. Classes will be a mix of lectures, discussion of recent work by students, and hands-on group projects at the intersection of ML and science.

Prerequisites: Students should come with familiarity with computer vision or machine learning as well as an enthusiasm for trying new data but no formal prerequisites are required.
CSCI-GA.3033-114
CS-GY 9223-I
(22062) Special Topics: Recent Developments in Algorithm Design Anupam Gupta R 2:00-4:30PM CIWW 202

The course covers several recent developments in algorithm design: e.g., recent trends in data structures, fast algorithms for shortest paths, matchings and flows, online and approximation algorithms, stochastic probing and sequencing problems, and algorithms for numerical linear algebra. Each of these topics will be covered in a set of 2-3 lectures by NYU faculty who are specialists in the respective areas.

Prerequisites: Graduate level knowledge of algorithms required. For example, Honors Analysis of Algorithms, or similar graduate-level Algorithms courses.
CSCI-GA.3033-115
DS-GA.3001-009
(23837) Special Topics: Embodied Learning and Vision Mengye Ren R 4:55-6:35PM 19W4 102

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-116
(23639) Special Topics: Emerging Topics in Natural Language Processing Eunsol Choi W 2:45-4:45PM 60 Fifth Ave C15

We will discuss emerging topics in natural language processing, focusing on understanding the development and usages of large-scale language models. This course is designed for graduate students and highly motivated undergraduates who are interested in NLP research. This course aims to teach and practice: (1) cutting-edge research in natural language processing. (2) how to formulate and evaluate NLP problems and develop solutions for them. (3) how to read and criticize research papers and communicate research both orally and in writing. The course is designed as an active seminar course with a semester-long final project element. Ideally, you should be excited about the intricacies of language understanding and willing to independently explore the literature. The course will cover various aspects of language understanding and generation, such as reasoning, knowledge, and uncertainty.

Prerequisites: The students are expected to have successfully completed a graduate level course in either (1) natural language processing, (2) deep learning or (3) machine learning. The students should be comfortable with basic fundamentals of machine learning, statistics, and deep learning programming (python programming in Pytorch or Jax).
CSCI-GA.3033-120
(18972) Special Topics: Interactive Proofs CANCELLED -

Interactive proofs played a major role in various developments in theoretical computer science and cryptography. In this course, we aim to study these computational models via the complexity theoretic lens, and in particular discuss the PCP Theorem and applications of it to Hardness of Approximation results. If time allows, we will discuss the quantum analogues of these computational models, and their relation to the Approximation Theory of Algebraic objects in general and Connes' Embedding Problem in particular.

Prerequisites: Linear algebra. Some knowledge in Complexity theory, Probability Theory (over finite spaces), and Abstract Algebra (in particular, Group Theory) will be helpful but not mandatory.
CSCI-GA.3033-121
(21994) Special Topics: Programming Parallel Algorithms Sam Westrick M 10:15-12:15PM 60 Fifth Ave C15

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-934
(22247) Special Topics: Multicore Processors: Architecture & Programming Recitation TBA F 12:30-1:20PM CIWW 101 Additional information: Recitation for Section 034

This is the recitation for section 034. Refer to the lecture for description.
CSCI-GA.3033-998
DS-GA.3001-010
(23838) Special Topics: Embodied Learning and Vision (Recitation) STAFF R 6:45-7:35PM 19W4 102

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-999
(5470) Special Topics: Introduction to Computer Vision Recitation STAFF R 4:55-5:45PM Silv 401 Additional information: Recitation for Section 099

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-9483
ECE-GY.9483
(23298) Special Topics: Efficient AI and Hardware Accelerator Design Sai Qian Zhang W 5:00-7:30PM Jacobs 775

The course will focus on recent advancements in the design of efficient neural networks, specifically on how to create and optimize AI models for improved performance, scalability, and resource efficiency. Students will explore key techniques like model compression, pruning, quantization, and model distillation for CNN, RNN, Transformer and LLM, aimed at reducing computational complexity and memory usage while maintaining accuracy. Additionally, the course will cover efficient training and inference methods, including distributed computing, parallelism, and low-precision computation, which are crucial for deploying AI on resource-limited devices such as smartphones or edge computing systems. Students will also study advanced hardware architectures of AI accelerators, gaining insights into the hardware implementation of neural network computations on these specialized systems.

Prerequisites: This course is open to both PhD and MS students. Prerequisites include prior knowledge of computer architecture (CSCI-UA.0201-001 or equivalent) and basic understanding of deep learning (CSCI-GA.2572-001 or equivalent).
CSCI-GA.3140-001
(5184) Abstract Interpretation Patrick Cousot M 4:55-6:55PM CIWW 202

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

Prerequisites: Some facility with logic, sets, linear algebra, graphs, formal languages, mathematical proofs, and programming languages is expected.
CSCI-GA.3205-001
(5140) Applied Cryptography & Network Security Mazdak Zamani W 4:55-6:55PM 60 Fifth Ave 110
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.3812-001
(5171) Information Technology Projects * Evan Korth W 5:00-8:00PM GCASL 275

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
(5141) 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
(5142) 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
(5143) 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
(5144) 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
(5145) 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
(5146) 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
(5147) 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
(5148) 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
(5149) 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
(5150) PhD Seminar: Cryptography ** Yevgeniy Dodis 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
(5151) PhD Seminar: Formal Methods Joseph Tassarotti 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
(5152) PhD Thesis Research ** STAFF -

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(5153) Internship In Computer Science MS* STAFF -

Participation in a programming project or research project conducted outside of the classroom 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 take 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
(5154) Internship in Computer Science PhD* STAFF -

Participation in a programming project or research project conducted outside of the classroom 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 take 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.

*

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.

Classroom Locations

Notice: This webpage is subject to change and will be revised and updated as necessary.