Course Schedule

Current semester Spring 2018, and future semesters are now available to view.

View undergraduate course grid View graduate course grid

Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1144-002
(2744) PAC II Recitation Srinidhi Goud Myadaboyina
Office Hours R 7:10-8:00PM CIWW 312

This course builds directly on the foundation developed in PAC I, covering the essentials of computer organization through the study of assembly language programming and C, as well as introducing the students to the analysis of algorithms. Topics include: (1) Assembly language programming for the Intel chip family, emphasizing computer organization, the Intel x86 instruction set, the logic of machine addressing, registers and the system stack. (2) Programming in the C language, a general-purpose programming language which also has low-level features for systems programming. (3) An introduction to algorithms, including searching, sorting, graph algorithms and asymptotic complexity. Examples and assignments reinforce and refine those first seen in PAC I and often connect directly to topics in the core computer science graduate courses, such as Programming Languages, Fundamental Algorithms, and Operating Systems

Prerequisites: <p>CSCI-GA 1133 or departmental permission.</p>
CSCI-GA.1170-002
DS-GA.1170
(2315) Fundamental Algorithms Recitation Ojas Deshpande R 8:10-9:00PM CIWW 109

Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.2110-002
(2317) Programming Languages Recitation Chirag Maheshwari R 7:10-8:00PM CIWW 109

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2262-001
(2320) Data Communications and Networks Jean-Claude Franchitti
Office Hours R 5:10-7:00PM CIWW 101

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

Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
CSCI-GA.2620-001
(2784) Networks and Mobile Systems Anirudh Sivaraman
Office Hours R 7:10-9:00PM 60 Fifth Ave C10

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.2946-001
DS-GA.3001-010
(23908) Mathematics of Data Science: Graphs and Networks Afonso Bandeira
Office Hours R 4:55-6:35PM 60 Fifth Ave 110

This is part of a two course series on Mathematics of Data Science. Each part can be taken independently, and they can be taken in any order. This part focus on algorithms on graphs and networks while the other in high dimensional data. This is a mostly self-contained research-oriented and fast-paced course designed for graduate students with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data. These often lie in overlaps of (Applied) Mathematics with: Computer Science, Electrical Engineering, Statistics, Operations Research and/or Statistical Physics. Each lecture will feature a couple of Mathematical Open Problem(s) with relevance in Data Science. The main mathematical tools used will be Probability and Linear Algebra, and a working knowledge of these subjects is required. There will also be some (although knowledge of these tools is not assumed) Graph Theory, Representation Theory, Applied Harmonic Analysis, among others. The topics treated will include Random Matrices, Approximation Algorithms, Convex Relaxations, Community detection in graphs, and several others.
CSCI-GA.3033-005
(2330) Special Topics: Big Data Application Development Suzanne McIntosh
Office Hours R 7:10-9:00PM CIWW 101

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

Prerequisites: Successful completion of the Realtime and Big Data Analytics course is a prerequisite for registering for this course. In addition, students should have: strong programming skills in Java, Python, or C++; experience using Hadoop; coursework in operating systems, networking, and algorithms; familiarity with Linux commands.
CSCI-GA.3033-010
(3146) Special Topics: Cloud Computing Jean-Claude Franchitti
Office Hours R 7:10-9:00PM CIWW 102

This is a capstone course.

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

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

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

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

The SaaS and aPaaS/PaaS topics covered in the course will familiarize students with the use of vendor-maintained applications and processes available on the Cloud on a metered on-demand basis in multi-tenant environments.

Through hands-on assignments and projects, students will learn how to configure and program IaaS services. They will also learn how to develop Cloud-based software applications on top of various Cloud platforms, how to integrate application-level services built on heterogeneous Cloud platforms, and how to leverage SaaS, aPaaS, and BPaaS solutions to build comprehensive end-to-end business solutions on the Cloud.

Prerequisites: Some programming experience in mainstream languages such as Java, Python, C#, or C++.
CSCI-GA.3033-014
(19894) Special Topics: Data Science for Health Lakshminarayanan Subramanian
Office Hours R 1:25-3:15PM CIWW 1302

Data Science for Health

This is a graduate level class on how data science can impact important health challenges facing society today. This is a project-oriented class that aims to cover how big data, machine learning algorithms, statistical analytics techniques and mobile applications have had a direct impact on different important facets of health. The classes will be centered around case studies around specific health topics and the influence of data science on each topic. The tentative sets of topics that will be covered in this course include: randomized control trials and measuring health outcomes, personalized health inference and monitoring, behavioral health, mobile health applications, wearable sensors and health monitoring, gene sequence analytics and health traits, hi-throughput sequence analytics, disease markers and progression analysis, disease surveillance systems, testing efficacy of drugs, precision medicine, data science for health information management systems and data science use-cases in medical imaging.

Prerequisites: CSCI-GA 1170 or an undergraduate algorithms class, basic knowledge of statistics and at least one programming language such as Python.
CSCI-GA.3033-018
(3153) Special Topics: Geometric Modeling Daniele Panozzo
Office Hours R 5:10-7:00PM 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 polygonal meshes, surface reconstruction, mesh improvement, mesh parametrization, discrete differential geometry, interactive shape editing, skinning animation, architectural and structure-aware geometric modeling, shape modeling with an eye on 3D printing. The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.

Prerequisites: Basic knowledge of Computer Graphics, experience with C++ programming. Some background in geometric computing is helpful, but not necessary. CSCI-UA 140 (or an equivalent undergraduate course in linear algebra)
CSCI-GA.3033-022
(24148) Special Topics: Deep Generative Models Rajesh Ranganath
Office Hours R 5:10-7:00PM CIWW 312

Generative models define procedures that produce samples of data. They can be used to learn representations, to handle exploration/exploitation tradeoffs, and to make use of the large amounts of unlabeled data available. Deep generative models use ideas from deep learning to build generative models and algorithms for learning them. This course will focus on some of the recent advances in deep generative models including deep latent variable models, variational autoencoders, autoregressive models, and generative adversarial networks. Students will gain experience in working with these methods and understanding the tradeoffs between them.

Prerequisites: CSCI-GA 2567/DS-GA 1003 Machine Learning; Knowledge of probability theory, statistics, and basic neural networks.
CSCI-GA.3850-004
(2341) 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.

*

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

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