Course Schedule

Current semester Fall 2017, and future semesters are now available to view.

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1121-001
(17297) Working with Data**** Deena Engel T 6:20-9:00PM 60 Fifth Ave C10

Course Description:

Students study the principles of relational database design and learn to build, populate, manipulate and query databases using SQL on datasets relevant to their interests. Students will also explore data presentation through data visualization. Not open to Graduate Computer Science, Information Systems, Mathematics or Scientific Computing students. For registration, please contact dhss@nyu.edu.
CSCI-GA.1133-001
(1127) PAC I* Evan Korth
Office Hours T 6:30-9:00PM CIWW 201

Course Description:

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
(1128) Fundamental Algorithms Alan Siegel
Office Hours T 5:10-7:00PM CIWW 109

Course Description:

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.2274-001
(2098) Advanced Computer Graphics Kenneth Perlin T 7:10-9:00PM 60 Fifth Ave 261

Course Description:

This is a capstone course based on computer graphics tools. The course covers a selection of topics that may include computer animation, gaming, geometric modeling, motion capture, computational photography, physically based simulation, scientific visualization, and user interfaces. Not all areas are available every semester; the choice of areas is determined by the instructor. The capstone project involves 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, CSCI-GA 2250 and CSCI-GA 2270
CSCI-GA.2434-001
(1136) Advanced Database Systems Dennis Shasha
Office Hours T 5:10-7:00PM 60 Fifth Ave 150

Course Description:
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
(1799) Foundations Of Machine Learning Mehryar Mohri
Office Hours T 5:10-7:00PM CIWW 102

Course Description:

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.2945-001
MATH-GA.2011-001
(1137) Adv Topics Num. Analysis: Gaussian elimination, Random functions and random differential equations, Chebyshev series, Rational functions, Quadrature, and ODEs Lloyd N. Trefethen
Office Hours T 1:25-3:15PM CIWW 512
Course Description:

Gaussian elimination, Random functions and random differential equations, Chebyshev series, Rational functions, Quadrature, and ODEs

This six-week course will be structured in an unusual way. Each of our six meetings will be independent. At each meeting, the first hour will be a lecture aimed at anyone interested in numerical analysis at a high level, organized around a well-known topic and mixing historical perspectives, recent developments, and always some new mathematics. The second hour will be for enrolled students only, a hands-on work session making use of Chebfun.
- Lecture 1. Gaussian elimination
- Lecture 2. Random functions and random differential equations
- Lecture 3. Chebyshev series
- Lecture 4. Rational functions
- Lecture 5. Quadrature
- Lecture 6. ODEs
CSCI-GA.2965-001
(1138) Heuristic Problem Solving Dennis Shasha
Office Hours T 7:10-9:00PM 60 Fifth Ave C14

Course Description:
This course revolves around several problems new to computer science (derived from games or puzzles in columns for Dr. Dobb’s Journal, Scientific American, and elsewhere). The idea is to train students to face a new problem, read relevant literature, and come up with a solution. The solution entails winning a contest against other solutions. The winner receives candy. The best solutions become part of an evolving “Omniheurist” Web site that is expected to get many visitors over the years. The course is for highly motivated, mathematically adept students. It is open to supported Ph.D. students and well-qualified master’s students. Class size has been around 10 in the past, and instructor and students have all gotten to know one another very well. Algorithmic and programming knowledge is the main prerequisite. It also helps to be familiar with a rapid prototyping language such as Matlab, Mathematica, K, or Python, or to be completely fluent in some other language.

Prerequisites: CSCI-GA 1170 and an ability to prototype algorithms rapidly.
CSCI-GA.3033-004
(1142) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Mohamed Zahran
Office Hours T 5:10-7:00PM CIWW 317

Course Description:

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. This course serves as a capstone for the MSCS program.
CSCI-GA.3033-008
(1889) Special Topics: Statistical Natural Language Processing Ankur Parikh
Office Hours T 5:10-7:00PM CIWW 512

Course Description:

In this course we will examine some of the core tasks in natural language processing, starting with simple word-based models for text classification and building up to rich, structured models for syntactic parsing and machine translation. In each case we will discuss recent research progress in the area and how to design efficient systems for practical user applications. There will be a focus on corpus-driven methods that make use of supervised and unsupervised machine learning methods and algorithms. We will explore statistical approaches based on graphical models and neural networks. In the course assignments, which will be updated this year to include more neural network modeling, you will construct basic systems and then improve them through a cycle of error analysis and model redesign. This course assumes a good background in basic probability and a strong ability and interest in building real systems.
CSCI-GA.3033-016
(2305) Special Topics: Big Data and ML Systems Lakshminarayanan Subramanian
Office Hours T 7:10-9:00PM CIWW 101

Course Description:

This class aims to cover introductory and recent concepts in big data and machine learning systems. The class will focus on a broad spectrum of big data computational problems, algorithms and platforms. The class will be centered around several big data case studies in commercial use to understand big data and machine learning tasks across different domains and provide an in-depth understanding of the problems and algorithms used in each domain. From the computational platform perspective, this class covers popular and widely used commercial platforms including Hadoop and Mapreduce variants, Spark, streaming platforms, Tensorflow, ML platforms (MLBase, MxNet) and GPU platforms. The goal of this class is to educate students about the algorithms and systems techniques used to build scalable big data and ML platforms and how these platforms are used in the real world.

This class covers the following broad topics:
a. Cloud Infrastructure Blocks: AWS, Mapreduce, Hadoop, Yarn
b. Big Data Algorithms: searching and indexing large data sets, implementing standard statistical algorithms, similar items, nearest neighbors, graph mining, network analytics, dimensionality reduction
c. Big Data Computation Platforms: Spark, TensorFlow, MLBase, MLib, MxNet, GPU computing
d. Scalable ML Algorithms: Implementing deep learning algorithms, graph and network analytics, scalable vision algorithms, scalable NLP algorithms
e. Domain Specific Applications and Case Studies: vision analytics engines, NLP analytics engines, search and graph analytics
CSCI-GA.3033-019
(17299) Special Topics: Cryptocurrencies and Decentralized Ledgers Joseph Bonneau
Office Hours T 7:10-9:00PM 60 Fifth Ave 150

Course Description:

Bitcoin, Ethereum, and other systems using decentralized ledgers (blockchains) have quickly grown to valuations of tens of billions of dollars. Their future potential has captured the imagination of researchers working on applications as diverse as banking and finance, voting, law, corporate governance, gambling and online gaming. This course will cover the technical concepts underlying these systems: append-only ledgers, decentralized consensus, smart contracts and zero-knowledge proof systems. Students will gain working knowledge of both Bitcoin and Ethereum through practical assignments. The course will also survey the wide variety of potential future applications.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java. An undergraduate course in cryptography or successful completion of CSCI-GA.3205 or CSCI-GA.3210 strongly recommended.
CSCI-GA.3110-001
(18812) Honors Programming Languages Patrick Cousot
Office Hours TR 3:30-4:45PM 60 Fifth Ave C15

Course Description:

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: CSCI-GA.2110 Programming Languages, CSCI-GA.3110 Honors Programming Languages
CSCI-GA.3520-001
(1146) Honors Analysis of Algorithms Subhash Khot
Office Hours TR 9:30-10:45AM CIWW 317

Course Description:
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.3850-006
(1801) PhD Seminar: Machine Learning** Mehryar Mohri
Office Hours T 3:45-5:00PM CIWW 1302

Course Description:
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
(6161) Intro To Computer Programming Nathan Hull
Office Hours TR 12:30-1:45PM CIWW 202

Course Description:

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 with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. 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. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-002
(6162) Intro To Computer Programming Craig Kapp
Office Hours TR 11:00-12:15PM 60 Fifth Ave 150

Course Description:

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 with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. 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. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-006
(6166) Intro To Computer Programming Amos Bloomberg
Office Hours TR 9:30-10:45AM CIWW 101

Course Description:

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 with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. 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. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-009
(7775) Intro To Computer Programming Jerry Waxman
Office Hours TR 3:30-4:45PM CIWW 517

Course Description:

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 with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. 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. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-012
(8522) Intro To Computer Programming Michell Cardona
Office Hours TR 8:00-9:15AM 60 Fifth Ave 110

Course Description:

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 with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. 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. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0004-002
(6169) Intro to Web Design & Computer Principles Alvaro Olsen
Office Hours TR 12:30-1:45PM 60 Fifth Ave 110

Course Description:
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
(6171) Intro to Web Design & Computer Principles Jason Aston
Office Hours TR 11:00-12:15PM CIWW 202

Course Description:
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
(7774) Intro to Web Design & Computer Principles Joshua Clayton
Office Hours TR 2:00-3:15PM CIWW 201

Course Description:
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.0004-007
(7974) Intro to Web Design & Computer Principles Adam Scher
Office Hours TR 8:00-9:15AM CIWW 101

Course Description:
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.0061-002
(8240) Web Development And Programming Sana Odeh TR 3:30-4:45PM CIWW 512

Course Description:

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 (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), and Introduction to Web Design and Computer Principles (CSCI-UA 4). Students that have successfully completed Applied Internet Technology (CSCI-UA 467) are NOT eligible to take this course.
CSCI-UA.0101-001
(6173) Intro To Computer Science Anasse Bari
Office Hours TR 9:30-10:45AM 60 Fifth Ave 110

Course Description:

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 (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-002
(6174) Intro To Computer Science Amos Bloomberg
Office Hours TR 11:00-12:15PM CIWW 512

Course Description:

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 (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-003
(6175) Intro To Computer Science Anasse Bari
Office Hours TR 3:30-4:45PM CIWW 102

Course Description:

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 (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-008
(8965) Intro To Computer Science Brett Peterson
Office Hours TR 8:00-9:15AM CIWW 102

Course Description:

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 (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0102-003
(6179) Data Structures Joanna Klukowska
Office Hours TR 11:00-12:15PM CIWW 101

Course Description:

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), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-006
(8050) Data Structures - Recitation John Ortega T 12:30-1:45PM CIWW 109

Course Description:

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), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-007
(8508) Data Structures Evan Korth
Office Hours TR 9:30-10:45AM 60 Fifth Ave 150

Course Description:

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), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0201-001
(6182) Computer Systems Organization Mohamed Zahran
Office Hours TR 11:00-12:15PM CIWW 109

Course Description:

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.0201-003
(7802) Computer Systems Organization Mohamed Zahran
Office Hours TR 3:30-4:45PM 60 Fifth Ave 150

Course Description:

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
(6184) Operating Systems Allan Gottlieb
Office Hours TR 2:00-3:15PM CIWW 109

Course Description:

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.0202-002
(18602) Operating Systems Yan Shvartzshnaider
Office Hours TR 2:00-3:15PM 60 Fifth Ave 110

Course Description:

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.0310-003
(18599) Basic Algorithms Victor Shoup
Office Hours TR 3:30-4:45PM 60 Fifth Ave 110

Course Description:

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); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0436-001
(6188) Computer Architecture Allan Gottlieb
Office Hours TR 3:30-4:45PM CIWW 101

Course Description:

The structure and design of computer systems. Basic logic modules and arithmetic circuits. Control unit design and structure of a simple processor; speed-up techniques. Storage technologies and structure of memory hierarchies; error detection and correction. Input/output structures, busses, programmed data transfer, interrupts, DMA, and microprocessors. Discussion of various computer architectures; stack, pipeline, and parallel machines; and multiple functional units. Not offered every academic year.

Prerequisites:
Computer Systems Organization (CSCI-UA 201) and Discrete Mathematics (MATH-UA 120).
CSCI-UA.0480-003
(8051) Special Topics: Applied Internet Technology Joseph Versoza
Office Hours TR 11:00-12:15PM 60 Fifth Ave 110

Course Description:

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
CSCI-UA.0480-004
(8052) Special Topics: Intro to Social Networking Bhubaneswar Mishra
Office Hours TR 2:00-3:15PM CIWW 1302

Course Description:

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
CSCI-UA.0480-005
(8505) Special Topics: Large Scale Web Applications Yair Sovran
Office Hours T 6:20-8:50PM Silv 401

Course Description:

This class will examine the challenges of scaling up a data-centric web application to serve millions of users. Students will learn and apply distributed computing concepts as they convert a simple monolithic web service to a scalable distributed architecture, with an emphasis on back-end components. Students will be introduced to the practice of scaling by distribution, and the exercises will have students apply a combination of popular open source technologies and code newly written by them, as they continue to augment the service. Lectures will cover topics such as Data models (Relational Databases vs No-SQL); Data partitioning; Caching; the RPC abstraction and RESTful APIs; distributed data processing pipelines; logging and monitoring; replication; and managed cloud technologies. We will also review existing popular systems that apply these concepts, as case studies. Topics: distributed computing, distributed storage systems, replication and consistency, Open Source technologies.

Prerequisites: CSCI-UA.0201
CSCI-UA.0480-006
(8513) Special Topics: Natural Language Processing Adam Meyers
Office Hours TR 8:00-9:15AM 60 Fifth Ave C10

Course Description:

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
CSCI-UA.0480-008
(18603) Special Topics: Computer Graphics Jeremie Dumas
Office Hours TR 9:30-10:45AM CIWW 202

Course Description:

This course provides an introduction to the field of Computer Graphics. The course will cover the basic mathematical concepts, study the interaction of light with geometry, and implement basic rendering algorithms such as ray tracing and rasterization. At the end of the semester, students will have built their own raytracer, and developed a real-time 3D computer graphics system using the OpenGL 4 graphics API.

Prerequisites: CSCI-UA 201 Computer Systems Organization, CSCI-UA-310 (Basic Algorithms) and MATH-UA-140 (Linear Algebra), or departmental consent.

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Indicates controlled enrollment (permission number required for registration). Contact your program advisor.
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Indicates controlled enrollment (permission number required for registration). Contact Santiago Pizzini (pizzini@cs.nyu.edu).
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Indicates controlled enrollment (permission number required for registration). Contact the instructor.

Classroom Locations

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