Course Schedule

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Number

Name

Professor(s)

Meet Time

Room

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

An accelerated introduction to the fundamental concepts of computer science for students who lack a formal background in the field. Topics include algorithm design and program development; data types; control structures; subprograms and parameter passing; recursion; data structures; searching and sorting; dynamic storage allocation and pointers; abstract data types, such as stacks, queues, lists, and tree structures; generic packages; and an introduction to the principles of object-oriented programming. The primary programming language used in the course will be Java. Students should expect an average of 12-16 hours of programming and related course work per week.
CSCI-GA.1170-001
DS-GA.1170
(2995) Fundamental Algorithms Richard Cole
Office Hours T 5:10-7:00PM ONLINE

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

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-012
DS-GA.1170
(26503) Fundamental Algorithms Recitation Chee Yap
Office Hours T 5:10-6:00PM 1555 Century 1505 Additional information: Open only to NYU Shanghai GoLocal students

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

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.2110-012
(26506) Programming Languages Recitation Yuxin Deng
Jing Liu T 6:30-7:20PM 16 Qi Xia 917 Additional information: Open only to NYU Shanghai GoLocal students

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2434-001
(3002) Advanced Database Systems Dennis Shasha
Office Hours T 5:10-7:00PM CIWW 109
This is a capstone course emphasizing large-scale database systems. This course studies the internals of database systems as an introduction to research and as a basis for rational performance tuning. Topics include concurrency control, fault tolerance, operating system interactions, query processing, and principles of tuning. Database capstone projects involve topics such as design, concurrency control, interactions, and tuning. These projects include some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2566-001
(19796) Foundations Of Machine Learning* Mehryar Mohri
Office Hours T 5:10-7:00PM ONLINE

This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are probability and general bounds; PAC model; VC dimension; perceptron, Winnow; support vector machines (SVMs); kernel methods; decision trees; boosting; regression problems and algorithms; ranking problems and algorithms; halving algorithm, weighted majority algorithm, mistake bounds; learning automata, Angluin-type algorithms; and reinforcement learning, Markov decision processes (MDPs).

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

An introduction to natural language processing, with an emphasis on methods for creating structured knowledge from text. Basic syntactic structures of English; constituent and dependency representations and parsers. Hidden Markov Models and maximum entropy models for part-of-speech and name tagging. Finite-state grammars and partial parsing. Probabilistic parsing. Lexical semantics. Relation and event extraction. Reference resolution. Machine translation. Supervised, semi-supervised, and active learning of linguistic models. Neural network models. There will be several pencil-and-paper exercises, several computer exercises, a final project, and a final exam.

Prerequisites: Some facility with Java and Python and a knowledge of the elements of probability and statistics is expected.
CSCI-GA.3033-091
DS-GA.3001-006
(23047) Special Topics in Data Science: Machine Learning for Healthcare Lab Aalad Manas Puli T 1:00-1:50PM Silv 405 Additional information: Lab for CSCI-GA.3033-083

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

Students must enroll into Lecture section 083 with Lab section 091.
CSCI-GA.3033-093
(26509) Special Topics: Introduction to Deep Learning Systems Parijat Dube
Office Hours T 7:10-9:00PM CIWW 101 Additional information: Syllabus for CSCI-GA.3033-093

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

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

Prerequisites: An introductory machine learning course. Knowledge of python is preferred but not necessary.
CSCI-GA.3110-001
(22063) Honors Programming Languages Patrick Cousot TR 3:30-4:45PM ONLINE
The course will introduce a panorama of programming languages concepts underlying the main programming language paradigms (such as imperative, functional, object-oriented, logic, concurrent, and scripting languages) and present in detail the formal methods (code semantics, specification, and verification) used in modern high quality assurance tools for software safety and security. A programming project (design and implementation of an interpreter/compiler for an dynamic object-oriented mini-language) will be programmed in OCaml, a multiparadigm language introduced at the beginning of the course.

Prerequisites: Permission of the instructor for master’s students
CSCI-GA.3205-001
(3831) Applied Cryptography & Network Security Zvi Kedem
Office Hours T 7:10-9:00PM ONLINE
This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.
CSCI-GA.3520-001
(3007) Honors Analysis of Algorithms Subhash Khot TR 9:30-10:45AM 60 Fifth Ave C15
Design of algorithms and data structures. Review of searching, sorting, and fundamental graph algorithms. In-depth analysis of algorithmic complexity, including advanced topics on recurrence equations and NP-complete problems. Advanced topics on lower bounds, randomized algorithms, amortized algorithms, and data structure design as applied to union-find, pattern matching, polynomial arithmetic, network flow, and matching.

Prerequisites: Permission of the instructor for master’s students.
CSCI-GA.3850-001
(4113) PhD Seminar: Cryptography ** Yevgeniy Dodis T 12:30-3:15PM ONLINE
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-UA.0002-001
(8509) Intro To Computer Programming (No Prior Experience) Hasan Aljabbouli
Office Hours TR 12:30-1:45PM ONLINE

An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major.
CSCI-UA.0002-002
(8510) Intro To Computer Programming (No Prior Experience) Joshua Clayton
Office Hours TR 11:00-12:15PM ONLINE

An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major.
CSCI-UA.0002-006
(8513) Intro To Computer Programming (No Prior Experience) Shaheer Mohammed Haroon
Office Hours TR 9:30-10:45AM ONLINE

An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major.
CSCI-UA.0002-009
(9803) Intro To Computer Programming (No Prior Experience) Saadia Lgarch
Office Hours TR 3:30-4:45PM ONLINE

An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major.
CSCI-UA.0002-012
(10257) Intro To Computer Programming (No Prior Experience) Michell Cardona
Office Hours TR 8:00-9:15AM ONLINE

An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major.
CSCI-UA.0004-001
(9942) Intro to Web Design & Computer Principles Adam Scher
Office Hours TR 8:00-9:15AM ONLINE
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0004-004
(8518) Intro to Web Design & Computer Principles Joshua Clayton
Office Hours TR 2:00-3:15PM ONLINE
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0060-001
(9825) Database Design And Implementation Joseph Versoza
Office Hours TR 12:30-1:45PM ONLINE

Introduces principles and applications of database design. Students learn to use a relational database system, learn implementations of database designs, and write programs in SQL. Students explore principles of database design and apply those principles to computer systems in general and in their respective fields of interest.

Prerequisites: Introduction to Computer Programming (CSCI-UA.0002).
CSCI-UA.0061-001
(8519) Web Development And Programming Craig Kapp
Office Hours T 6:20-8:50PM ONLINE

Provides a practical approach to web technologies and programming. Students build interactive, secure, and powerful web programs. Covers client and server side technologies for the web.

Prerequisites: Introduction to Computer Programming (CSCI-UA.0002) and Introduction to Web Design & Computer Principles (CSCI-UA.0004).
CSCI-UA.0101-001
(8520) Intro To Computer Science Anasse Bari
Office Hours TR 2:00-3:15PM ONLINE
How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (CSCI-UA 2) or departmental permission assessed by placement exam.
CSCI-UA.0101-003
(8522) Intro To Computer Science Hilbert Gene Locklear
Office Hours TR 3:30-4:45PM ONLINE
How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (CSCI-UA 2) or departmental permission assessed by placement exam.
CSCI-UA.0101-008
(10379) Intro To Computer Science Nader Nassar
Office Hours TR 8:00-9:15AM ONLINE
How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (CSCI-UA 2) or departmental permission assessed by placement exam.
CSCI-UA.0102-001
(8526) Data Structures Anasse Bari
Office Hours TR 11:00-12:15PM ONLINE
Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101).
CSCI-UA.0102-003
(8528) Data Structures Young Kun Ko
Office Hours TR 12:30-1:45PM ONLINE
Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101).
CSCI-UA.0102-005
(10709) Data Structures Joanna Klukowska
Office Hours TR 9:30-10:45AM ONLINE
Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101).
CSCI-UA.0102-013
(25219) Data Structures - Recitation Alankrith Krishnan T 12:30-1:45PM CIWW 102 Additional information: Recitation for Section 1
Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101).
CSCI-UA.0102-028
(27153) Data Structures - Recitation Abhinav Gupta T 12:30-1:45PM ONLINE Additional information: Recitation for Section 1
Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101).
CSCI-UA.0201-003
(9824) Computer Systems Organization Allan Gottlieb
Office Hours TR 9:30-10:45AM ONLINE
Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0201-012
(25206) Computer Systems Organization - Recitation Sanjana Jangnure T 3:30-4:45PM Silv 206 Additional information: Recitation for Section 1
Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0202-001
(8531) Operating Systems Allan Gottlieb
Office Hours TR 12:30-1:45PM ONLINE
Covers the principles and design of operating systems. Topics include process scheduling and synchronization, deadlocks, memory management (including virtual memory), input/output, and file systems. Programming assignments.

Prerequisites: Computer Systems Organization (CSCI-UA 201).
CSCI-UA.0310-001
(8532) Basic Algorithms Victor Shoup
Office Hours TR 11:00-12:15PM ONLINE
Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

Prerequisites: Data Structures (CSCI-UA 102) and Discrete Mathematics (MATH-UA 120).
CSCI-UA.0310-004
(19831) Basic Algorithms - Recitation Alan Siegel
Office Hours T 3:30-4:45PM ONLINE
Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

Prerequisites: Data Structures (CSCI-UA 102) and Discrete Mathematics (MATH-UA 120).
CSCI-UA.0472-001
(10749) Artificial Intelligence Ernest Davis
Office Hours TR 3:30-4:45PM ONLINE
Many cognitive tasks that people can do easily and almost unconsciously have proven extremely difficult to program on a computer. Artificial intelligence tackles the problem of developing computer systems that can carry out these tasks. Focus is on three central areas in AI: representation and reasoning, machine learning, and natural language processing.

Prerequisites: Computer Systems Organization (CSCI-UA 201) and Basic Algorithms (CSCI-UA 310).
CSCI-UA.0476-001
(21983) Processing Big Data for Analytics Applications Ann Malavet TR 2:00-3:15PM ONLINE

Introduces platforms, tools, and architectures that facilitate scalable management and processing of vast quantities of data. Explores open source tools enabling the efficient acquisition, distributed storage, and processing of big data. Provides hands-on experience with distributed processing Apache solutions such as Hadoop MapReduce, HBase, Hive, Impala, Pig, core Spark, Spark SQL, and Spark Streaming. Other Apache big data tools covered are Sqoop, Oozie, Zookeeper, Flume, and Kafka.

Prerequisites: (CSCI-UA 201) Computer Systems Organization and (CSCI-UA 310) Basic Algorithms. Familiarity with Linux commands and SQL will be helpful but is not required.
CSCI-UA.0480-046
(10009) Special Topics: Intro to Social Networking Bhubaneswar Mishra
Office Hours TR 3:30-4:45PM ONLINE

Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. Their utilities have been enhanced by their ability to generate massive amount of personal data that need to be analyzed and disseminated quickly. The World Wide Web enables information flows among vast number of humans; Facebook, LinkedIn, etc. connect small groups of friends; amazon, eBay, etc. provide opportunities for trading, etc. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorum-sensing, swarming, flocking, social grooming, gossip, etc. Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant. This course will introduce the tools, analytics and algorithms for the study of networks and their data. It will show how certain common principles permeate the functioning of these diverse networks: e.g., issues related to robustness, fragility, and interlinkages etc.

Prerequisites: CSCI-UA.0201 with a grade of C or better
CSCI-UA.0480-070
(10696) Special Topics: Data Science for Health Lakshminarayanan Subramanian
Office Hours T 6:20-8:50PM Silv 208

This is an undergraduate level class on how data science can impact important health challenges facing society today. This is a project-oriented class that aims to cover data science techniques that 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 class will have three parts: Personal and Community Health, (5 weeks) Disease Analytics, (6 weeks) and Genomics and Health (3 weeks).

This class is a project oriented class and every student is expected to do a project either in groups of two or three. Project ideas will be discussed in class and students can also choose their own project ideas in consultation with the faculty.

Prerequisites: The class is open for undergraduate students from computer science, mathematics and related fields. Students are expected to be proficient in programming in Python. Pre-requisites include: CSCI-UA.201 Computer Systems Organization, MATH-UA.121 Calculus I or equivalent and MATH-UA.140 Linear Algebra or equivalent. Exposure to probability and statistics concepts is not essential but can be helpful.
CSCI-UA.0480-571
(10248) Special Topics: Natural Language Processing Adam Meyers
Office Hours TR 8:00-9:15AM ONLINE

Natural Language Processing (aka Computational Linguistics) is an inter-disciplinary field applying methodology of computer science and linguistics to the processing of natural languages (English, Chinese, Spanish, Japanese, etc.). Typical applications include: information extraction (automatically finding information from text); information retrieval (web searches and other applications involving the automatic selection of "relevant" documents); sentiment analysis (automatic extraction of opinions about a set of issues); and machine translation (automatically translating one natural language to another). Much of the best work in the field combines two methodologies: (1) automatically acquiring statistical information from one set of "training" documents to use as the basis for probabilistically predicting the distribution of similar information in new documents; and (2) using manually encoded linguistic knowledge. For example, many supervised methods of machine learning require: a corpus of text with manually encoded linguistic knowledge, a set of procedures for acquiring statistical patterns from this data and a transducer for predicting these same distinctions in new text. This class will cover linguistic, statistical and computational aspects of this exciting field.

Prerequisites: CSCI-UA.0201 with a grade of C or better

*

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.