Course Schedule

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-002
DS-GA.1170
(2407) Fundamental Algorithms Recitation Abhinav Tamaskar W 5:10-6:00PM CIWW 101

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.2250-002
(2985) Operating Systems Shameem Akhter
Office Hours W 7:10-9:00PM CIWW 201
The topics covered include a review of linkers and loaders and the high-level design of key operating systems concepts such as process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging and segmentation; and I/O and file systems, with examples from Unix/Linux and Windows. Programming assignments may require C, C++, Java, or C#.
CSCI-GA.2433-001
DS-GA.2433-001
(2414) Database Systems Zvi Kedem
Office Hours W 5:10-7:00PM CIWW 109
Database system architecture. Modeling an application and logical database design. The relational model and relational data definition and data manipulation languages. Design of relational databases and normalization theory. Physical database design. Concurrency and recovery. Query processing and optimization.
CSCI-GA.3033-001
(2419) Special Topics: Big Data Science Anasse Bari
Office Hours W 5:10-7:00PM 60 Fifth Ave C15

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

Prerequisites: Strong programming skills in the Java language and fundamentals of linear algebra.
CSCI-GA.3033-003
(19033) Special Topics: Production Quality Software Michael Schidlowsky
Office Hours W 5:10-7:00PM CIWW 317

In this course we study the craft of software engineering. The semester begins with a thorough review of Java and the Java Virtual Machine. We concentrate on library components and language features most relevant to typical engineering projects. Later lectures cover more sophisticated topics, including concurrency and thread-safety, writing tests, object-oriented programming, and design patterns. The course also covers non-technical topics that are critical to software development including how to write with clarity, managing bugs, successfully developing and reinforcing salutary engineering habits, the nature of human productivity as it relates specifically to engineering, and communicating effectively with teammates. Lessons are reinforced with weekly assignments that are meticulously graded, with some submissions critiqued in classroom code reviews.
CSCI-GA.3033-016
(3047) Special Topics: Multicore Processors: Architecture and Programming Mohamed Zahran
Office Hours W 5:10-7:00PM CIWW 312

This is a capstone course.

The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable.
CSCI-GA.3033-023
(3200) Special Topics: High Performance Machine Learning Ulrich Finkler
Office Hours
Wei Zhang W 7:10-9:00PM CIWW 312

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.3033-024
(3349) Special Topics: Big Data and ML Systems Lakshminarayanan Subramanian
Office Hours W 7:10-9:00PM CIWW 102
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, 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:
1. Cloud Infrastructure Blocks: AWS
2. Big Data Algorithms: searching and indexing large data sets, implementing standard statistical algorithms, similar items, nearest neighbors, graph mining, network analytics, dimensionality reduction
3. Big Data Computation Platforms: Spark, TensorFlow, MLBase, MLib, MxNet, GPU computing
4. Scalable ML Algorithms: Implementing deep learning algorithms, graph and network analytics, scalable vision algorithms, scalable NLP algorithms
5. Domain Specific Applications and Case Studies: vision analytics engines, NLP analytics engines, search and graph analytics
CSCI-GA.3033-026
(19032) Special Topics: Practical Computer Security Kevin Chen
Office Hours W 7:10-9:00PM CIWW 317

This course teaches the security mindset and practical security concepts that are essential for software engineers, software developers, quality assurance testers and IT managers. The course discusses various attack techniques and defenses for real world applications and the security activities in the SDLC. Its main topics include: memory safety (buffer overflow, return-oriented programming, etc), web security (XSS, CSRF, SQL Injection, etc), malware, threat modeling, security principles, secure coding practices (fuzzing, sandboxing, etc) and network security. Course projects and assignments may require Assembly, C, Python, JavaScript, HTML, SQL and/or other scripting languages.

Prerequisites: Programming experience in C/C++, JavaScript, Python, and SQL; basic understanding of assembly language; working knowledge of web application development.
CSCI-GA.3812-001
(2421) Information Technology Projects * Evan Korth
Office Hours W 6:00-9:00PM CIWW 512

This is a capstone course that connects students directly with real-world information technology problems. The goal of this course is to teach the skills needed for success in real-world information technology via a combination of classroom lectures and practical experience with large projects that have been specified by local “clients.” The typical clients are primarily companies, but can also be government agencies or nonprofit organizations. Each project lasts for the entire semester and is designed to involve the full software project life cycle. Examples of such projects are development of software to solve a business problem, including specifying requirements, writing and testing prototype code, and writing a final report; and evaluation of commercial software to be purchased to address a business problem, including gathering requirements, designing an architecture to connect the new software with existing systems, and assessing the suitability of available software products.

Prerequisites: For MS in IS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms and two of the following three courses: CSCI-GA 2262 Data Communications & Networks; CSCI-GA 2250 Operating Systems; CSCI-GA 2433 Database Systems. For MS in CS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms, CSCI-GA 2110 Programming Languages, and CSCI-GA 2250 Operating Systems. PERMISSION OF THE DEPARTMENT REQUIRED.
CSCI-GA.3850-007
(19895) PhD Seminar: Applications of Contextual Integrity** Yan Shvartzshnaider
Office Hours W 3:30-4:45PM 60 Fifth Ave 446
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-UA.0002-001
(7667) Intro To Computer Programming (No Prior Experience) Dakota Hernandez
Office Hours MW 3:30-4:45PM CIWW 201

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
(9501) Intro To Computer Programming (No Prior Experience) Craig Kapp
Office Hours MW 9:30-10:45AM CIWW 109

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
(8811) Intro To Computer Programming (No Prior Experience) David Gochfeld
Office Hours MW 9:30-10:45AM 60 Fifth Ave C10

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
(9502) Intro To Computer Programming (No Prior Experience) Adam Meyers
Office Hours MW 8:00-9:15AM 60 Fifth Ave C10

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
(20030) Intro To Computer Programming (No Prior Experience) Na'im Tyson
Office Hours W 6:20-8:50PM 60 Fifth Ave C12

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
(24536) Intro To Computer Programming (No Prior Experience) Deena Engel
Office Hours MW 2:00-3:15PM CIWW 101

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
(7671) Intro to Web Design & Computer Principles Alvaro Olsen
Office Hours MW 8:00-9:15AM 60 Fifth Ave 110
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
(8916) Intro to Web Design & Computer Principles Lee-Sean Huang
Office Hours MW 3:30-4:45PM CIWW 101
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
(9384) Intro to Web Design & Computer Principles Lee-Sean Huang
Office Hours MW 11:00-12:15PM CIWW 102
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.0061-001
(8723) Web Development And Programming Craig Kapp
Office Hours MW 12:30-1:45PM CIWW 201

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.0101-004
(7677) Intro To Computer Science Hilbert Gene Locklear
Office Hours MW 3:30-4:45PM CIWW 102
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
(8814) Intro To Computer Science Candido Cabo
Office Hours MW 12:30-1:45PM CIWW 202
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.0101-006
(9071) Intro To Computer Science Amos Bloomberg
Office Hours MW 2:00-3:15PM CIWW 317
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
(10064) Intro To Computer Science Nader Nassar
Office Hours MW 8:00-9:15AM CIWW 102
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
(7678) Data Structures Evan Korth
Office Hours MW 12:30-1:45PM 60 Fifth Ave 150
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
(7680) Data Structures Young Kun Ko
Office Hours MW 9:30-10:45AM CIWW 102
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.0102-006
(9102) Data Structures - Recitation Alankrith Krishnan W 3:30-4:45PM Meyr 122
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.0102-008
(9285) Data Structures - Recitation Abhinav Gupta 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.0201-001
(7683) Computer Systems Organization Douglas Moody
Office Hours MW 9:30-10:45AM CIWW 202
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
(9679) Computer Systems Organization Jean-Claude Franchitti
Office Hours MW 3:30-4:45PM CIWW 109
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.0201-008
(10258) Computer Systems Organization - Recitation Justin Mae W 3:30-4:45PM CIWW 202
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
(9674) Operating Systems Yan Shvartzshnaider
Office Hours MW 11:00-12:15PM CIWW 109
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
(7688) Basic Algorithms Seyed Nassajianmojarrad
Office Hours MW 11:00-12:15PM CIWW 101
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.0310-006
(9675) Basic Algorithms - Recitation Alex Bienstock W 12:30-1:45PM CIWW 109
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.0473-001
(9504) Intro To Machine Learning Kyunghyun Cho
Office Hours MW 11:00-12:15PM 60 Fifth Ave 150

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), Probability and Statistics (MATH-UA.235), and Linear Algebra (MATH-UA.140).
CSCI-UA.0476-001
(19847) Processing Big Data for Analytics Applications Suzanne McIntosh
Office Hours MW 4:55-6:10PM 7E12 LL23

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.0480-002
(7691) Special Topics: iOS Programming Nathan Hull
Office Hours MW 3:30-4:45PM CIWW 317

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

In this course, students will learn to create applications for Apple’s iOS on both the iPhone and the iPad using Objective-C, Apple's new programming language Swift, and the iOS SDK. Since its introduction, the Apple iOS SDK has proved to be a powerful platform upon which to build sophisticated applications. Without actually having to own an iPhone or an iPad, students will be able to build and test their applications on Apple Macs using the freely available compiler and simulator, XCode. Students will become proficient in the object-oriented language Objective-C, Swift, Apple iOS Frameworks, and the XCode development environment. This is a new, dynamic, constantly-evolving topic, and students will be at the forefront a new technological advancement.
CSCI-UA.0480-003
(8815) Special Topics: Parallel Computing Mohamed Zahran
Office Hours MW 2:00-3:15PM CIWW 109

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

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.
CSCI-UA.0480-005
(9106) Special Topics: Algorithmic Problem Solving - Recitation Kunal Khatri W 8:00-9:15AM CIWW 101

Prerequisites: CSCI-UA.0201 and CSCI-UA.310 with a grade of C or better.

Many of the top firms in the technological and financial sectors are using algorithmic problems as interview questions for assessing candidate skill. In this course we take this idea one step further and use algorithmic problem solving as way to hone programming skills. Students will use the material covered in the data structures and algorithms courses and learn new algorithmic techniques to solve challenging problems quickly. Each week will be devoted to a particular type of algorithm. Weekly problem sets will reinforce the lecture, and require students to implement their solutions in Java or C++. Students completing this course will be well prepared to participate in programming competitions such as the ACM Inter-Collegiate Programming Contest, TopCoder, and Google's Code Jam.
CSCI-UA.0480-008
(9677) Special Topics: Applied Internet Technology Joseph Versoza
Office Hours MW 11:00-12:15PM 12WV L120

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

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.
CSCI-UA.0480-009
(9846) Special Topics: Natural Language Processing Adam Meyers
Office Hours MW 12:30-1:45PM Meyr 122

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

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.
CSCI-UA.0480-010
(10065) Special Topics: Open Source Software Development Joanna Klukowska
Office Hours MW 3:30-4:45PM CIWW 517

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

This course prepares students to become active participants in open source projects. It begins with an overview of the philosophy and brief history of open source development, followed by an in-depth look at different types of open source projects and the study of various tools involved in open source development. In particular, it covers the collaborative nature of open source projects, community structure, version control systems, licensing, intellectual property, types of contributions (programming and non-programming) and the tool-chains that enable such contributions. The students are expected to contribute to existing open source projects.
CSCI-UA.0480-011
(10493) Special Topics: Introduction to Computer Security Joseph Bonneau
Office Hours MW 12:30-1:45PM CIWW 102

Prerequisites: CSCI-UA.0201 with a grade of C or better. Recommended: CSCI-UA.202 Operating Systems.

This course will cover basic principles of computer security and security engineering. To facilitate understanding, this course will consider security both from an attacker's perspective (threat modeling) and the defender's perspective (building and deploying secure systems). Specific topics will include operating system security, network security, web security, and mobile device security. Course projects will focus both on writing secure code and exploiting insecure code.
CSCI-UA.0480-014
(21937) Special Topics: iOS Programming Nathan Hull
Office Hours MW 12:30-1:45PM CIWW 101
Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.

Prerequisites: Topics determine prerequisites.

*

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.
****

Indicates controlled enrollment (permission number required for registration). Contact dhss@nyu.edu for assistance.