Course Schedule

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Number

Name

Professor(s)

Meet Time

Room

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

An accelerated introduction to the fundamental concepts of computer science for students who lack a formal background in the field. Topics include algorithm design and program development; data types; control structures; subprograms and parameter passing; recursion; data structures; searching and sorting; dynamic storage allocation and pointers; abstract data types, such as stacks, queues, lists, and tree structures; generic packages; and an introduction to the principles of object-oriented programming. The primary programming language used in the course will be Java. Students should expect an average of 12-16 hours of programming and related course work per week.
CSCI-GA.1170-003
DS-GA.1170
(3690) Fundamental Algorithms Alexander Alekseyev
Office Hours W 5:10-7:00PM ONLINE

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

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.2110-001
(2997) Programming Languages Benjamin Goldberg
Office Hours W 5:10-7:00PM ONLINE

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-011
(26504) Programming Languages Yuxin Deng
Jing Liu W 5:10-7:00PM 16 Qi Xia 917

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2621-001
(4063) Distributed Systems Aurojit Panda
Office Hours W 5:10-7:00PM ONLINE

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

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

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

Prerequisites: 1. Undergraduate Operating Systems or Computer Systems Organization; 2. programming experience; 3. mathematical maturity or the willingness to work through simple proofs.
CSCI-GA.2820-001
(3828) DevOps and Agile Methodologies John Rofrano W 5:10-7:00PM ONLINE

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2820-002
(3890) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM ONLINE

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2945-001
MATH-GA 2011-001 Advanced Topics In Numerical Methods: Monte Carlo Methods Jonathan Goodman
Office Hours W 5:10-7:00PM CIWW 1302
Topics vary each semester.

Prerequisites: Topics determine prerequisites.
CSCI-GA.3033-025
(3005) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Mohamed Zahran
Office Hours W 5:10-7:00PM CIWW 201

This course serves as a capstone for the MSCS program.

In this course, we will cover architectural aspects and capabilities of modern GPUs (graphics processing unit) and will learn the architecture and the programming of GPUs to solve different type of problems. Many computations can be performed much faster on the GPU than on a traditional processor. This is why GPUs are present now in almost all computers; and the majority of Top 500 supercomputers in the world are built around GPUs. GPUs are no longer restricted to graphics applications but are now used for a diverse set of applications and domains. This introduces the concept of general-purpose GPUs or GPGPUs, which is the main subject of this course.
CSCI-GA.3033-061
(3641) Special Topics: Predictive Analytics Anasse Bari
Office Hours W 5:10-7:00PM CIWW 109

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

Applications from bio-informatics, social networks analytics, and text mining will be covered. Highlights from industrial use cases will be covered to demonstrate how Predictive Analytics relates to improving business performance and impacting better decisions. This is an introductory course that will provide students with basic skills of the new generation of data scientists that will allow them to structure, analyze and derive useful insights from data that could help make better decisions.
CSCI-GA.3033-084
(3716) Special Topics: High Performance Machine Learning Ulrich Finkler
Office Hours W 7:10-9:00PM ONLINE

This is a capstone course for the MSCS program.

One of the key ingredients of the success of Machine Learning is the ability to perform computations on very large amounts of training data. High Performance Computing (HPC) is where cutting edge technology (GPUs, low latency interconnects, etc.) is applied to the solution of computing intensive problems. Today, the application of High Performance Computing techniques to ML algorithms is a fundamental driver for the progress of Artificial Intelligence. In this course, you will learn HPC techniques that are typically applied to supercomputing software, and how they are applied to obtain the maximum performance out of ML algorithms.

Topics: ML Hardware and Software stack, Performance analysis methodology, ML algorithms performance, CUDA, cuDNN, Pytorch, MPI, Parallel and Distributed ML algorithms.

Prerequisites: Operating Systems and Computer Architecture: CSCI-GA 2250 or good understanding of Computer Architecture, C/C++ programming, OS design, process stack/heap, threads, file-system, IO, Networks. Python and C languages: good programming skills in Python and C. Machine Learning and Neural Networks Algorithms: The course is focused on the performance rather than the algorithmic aspects. A basic explanation of the algorithms will be part of the course. However, it is strongly recommended to start the course with a basic understanding of the following algorithms: logistic regression, neural networks, convolutional neural networks.
CSCI-GA.3033-087
DS-GA.3001-011
(4232) Special Topics in Data Science: Bayesian Machine Learning Andrew Gordon Wilson
Office Hours W 5:00-6:40PM HEBU CR1

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

Students must enroll into Lab section 092 with Lecture section 087.
CSCI-UA.0002-003
(8511) Intro To Computer Programming (No Prior Experience) David Gochfeld
Office Hours MW 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.0002-004
(10675) Intro To Computer Programming (No Prior Experience) Dakota Hernandez
Office Hours MW 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.0002-005
(8512) Intro To Computer Programming (No Prior Experience) Dakota Hernandez
Office Hours W 5:00-7:30PM 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.0002-007
(8514) Intro To Computer Programming (No Prior Experience) Na'im Tyson
Office Hours MW 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.0002-008
(9801) Intro To Computer Programming (No Prior Experience) Craig Kapp
Office Hours MW 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.0002-011
(10033) Intro To Computer Programming (No Prior Experience) Adam Meyers
Office Hours MW 2:00-3:15PM 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.0003-001
(11189) Intro to Computer Programming (Limited Prior Experience) Joseph Versoza
Office Hours MW 9:30-10:45AM ONLINE

This course introduces object-oriented programming, recursion, and other important programming concepts to students who already have had some exposure to programming in the context of building applications using Python. Students will design and implement Python programs in a variety of applied areas.

Prerequisites: This course is intended for students with limited programming experience who have not placed into CSCI-UA.0101. Students may not receive credit for both CSCI-UA.0002 and CSCI-UA.0003.
CSCI-UA.0004-002
(8516) Intro to Web Design & Computer Principles Alvaro Olsen
Office Hours MW 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.0004-003
(8517) Intro to Web Design & Computer Principles Lee-Sean Huang
Office Hours MW 11:00-12:15PM 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.0004-005
(9666) Intro to Web Design & Computer Principles Alvaro Olsen
Office Hours MW 12:30-1:45PM 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.0004-006
(9802) Intro to Web Design & Computer Principles Sofya Yuditskaya
Office Hours MW 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.0101-004
(8523) Intro To Computer Science Hilbert Gene Locklear
Office Hours MW 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.0101-005
(9696) Intro To Computer Science Teseo Schneider
Office Hours MW 11:00-12:15PM 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.0101-007
(9944) Intro To Computer Science Candido Cabo
Office Hours MW 12:30-1:45PM 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.0102-002
(8527) Data Structures - Recitation Alankrith Krishnan W 8:00-9:15AM CIWW 109
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.0102-010
(11252) Data Structures - Recitation Kunal Khatri W 8:00-9:15AM ONLINE Additional information: Recitation for Section 5
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.0102-024
(27148) Data Structures - Recitation Abhinav Gupta W 8:00-9:15AM 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.0201-001
(8529) Computer Systems Organization Douglas Moody
Office Hours MW 11:00-12:15PM 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.0201-005
(10296) Computer Systems Organization Jean-Claude Franchitti
Office Hours MW 3:30-4:45PM 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.0201-007
(10755) Computer Systems Organization Jinyang Li MW 12:30-1:45PM 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.0201-010
(25204) Computer Systems Organization - Recitation Arahant Ashok Kumar W 11:00-12:15PM 60 Fifth Ave C15 Additional information: Recitation for Section 7
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.0202-002
(10543) Operating Systems Mohamed Zahran
Office Hours MW 9:30-10:45AM 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.0310-002
(8533) Basic Algorithms - Recitation Alex Bienstock W 12:30-1:45PM 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.0310-003
(10542) Basic Algorithms Alan Siegel
Office Hours MW 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.0380-001
(8534) Topics of General Interest: Interactive Computing Craig Kapp
Office Hours MW 9:30-10:45AM ONLINE

In this course you will learn how to develop multimedia applications for both the desktop and the web. The course focuses on general programming techniques with an emphasis on the production of interactive graphical environments. In addition, a number of advanced topics will be covered including image processing, video games, simulations, basic computer vision, augmented reality (AR), virtual reality (VR) and data visualization techniques.

Prerequisites: CSCI-UA.0002, CSCI-UA.0004 or instructor permission
CSCI-UA.0473-001
(11269) Intro To Machine Learning Benjamin Peherstorfer
Office Hours MW 11:00-12:15PM CIWW 109

This exciting and fast-evolving field of computer science has many recent consumer applications (e.g., Microsoft Kinect, Google Translate, IPhone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). Students learn the theoretical foundations and how to apply machine learning to solve new problems.

Prerequisites: Data Structures (CSCI-UA.102), Linear Algebra (MATH-UA.140) and Probability and Statistics (MATH-UA.235). Students may take any of the following courses in place of MATH-UA.235: Statistics (ECON-UA 18), Analytical Statistics (ECON-UA 20), Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Statistics for Business Control and Regression Models (STAT-UB 103)
CSCI-UA.0480-034
(10677) Special Topics: Applied Internet Technology Joseph Versoza
Office Hours MW 12:30-1:45PM ONLINE

A practical introduction to creating modern web applications. Covers full stack web development - including topics such as database / data model design, MVC architecture, templating, handling user input, asynchronous processing, and client-side interactivity. Students will use current server and client-side web frameworks to build dynamic, data-driven sites. Various tools to support development will also be introduced, such as version control and build systems. Basic knowledge of HTML and CSS and familiarity with command line tools are recommended.

Prerequisites: CSCI-UA.0201 with a grade of C or better
CSCI-UA.0480-041
(10418) Special Topics: Computer Graphics Kenneth Perlin
Office Hours MW 11:00-12:15PM ONLINE

There are many courses that can teach you how to use commercial computer graphics packages and APIs. This course, in contrast, will teach you how to build 3D computer graphics from the ground up. This will include 3D modeling, animation, and rendering. At the end of the semester you will have built your own complete working real-time 3D computer graphics systems that run in web browsers.

Prerequisites: CSCI-UA 201 Computer Systems Organization, CSCI-UA-310 Basic Algorithms and MATH-UA-140 Linear Algebra
CSCI-UA.0480-051
(10600) Special Topics: Parallel Computing Mohamed Zahran
Office Hours MW 2:00-3:15PM CIWW 109

Most of us have learned to program a single microprocessor (single core) using a high-level programming language like C/C++, Java, ... This is called sequential programming. We feel very comfortable with this because we think in a sequential way and give the machine statements to be executed in sequence. However, this has to change. A microprocessor with single core no longer exists in almost all computers we are using today (including your tablets and smart phones). Most of our devices are now multicore processors. A multicore processor contains several cores (called CPUs or cores) on-chip. To make the best use of these multicore chips we need to program them in-parallel. Sequential programming, for all platforms from smartphones to supercomputers, is falling out of fashion and taking back-seat to parallel programming. How to think in parallel? How to write code in parallel to make the best use of the underlying hardware? How is that new hardware different from the traditional one? What will the future be for the software and hardware? This is the topic of this course.

Prerequisites: CSCI-UA.0201 with a grade of C or better
CSCI-UA.0480-062
(10921) Special Topics: Computer Networks Anirudh Sivaraman
Office Hours MW 11:00-12:15PM ONLINE

This course will introduce students to computer networks, using today's Internet as an example. Topics covered include socket programming, routing, forwarding, reliable transmission, congestion control, and medium access control. The course will involve a good deal of programming in Python. Familiarity with programming, algorithms, and basic probability is expected.

Prerequisites: CSCI-UA.201 Computer Systems Organization, CSCI-UA.310 Basic Algorithms and MATH-UA.120 Discrete Mathematics
CSCI-UA.0480-071
(10248) Special Topics: Collaborating Remotely: Build Software from Anywhere Evan Korth
Office Hours MW 12:30-1:45PM ONLINE

In this course students will build software using modern software collaboration tools and methodologies widely used in industry. The first part (approximately 1/3rd) of the course will include a survey of some of the best software development collaboration tools and how to use them. Teams of students will explore the tools by building a sample project in Java. The rest of the course will be software development in teams using Agile methodologies. Students will choose their project from an assortment selected by the professor and their own proposals. Upon completion, students should have a portfolio piece.

Prerequisites: CSCI-UA.201 Computer Systems Organization
CSCI-UA.0480-572
(22266) Special Topics: Natural Language Processing Adam Meyers
Office Hours MW 12:30-1:45PM 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.