Course Schedule

Current semester Spring 2025, and future semesters are now available to view.

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• CSCI-GA.1170-​002
  DS-GA.1170-002
  (5116) Fundamental Algorithms Recitation  Bingwei Zhang R 5:55-6:45PM Meyr 122 Additional information: Recitation for Section 001
  

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​005
  (5119) Fundamental Algorithms Recitation  Eli Goldin R 3:45-4:35PM 60 Fifth Ave 110 Additional information: Recitation for Section 004
  

  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.1180-​001
  (5123) Mathematical Techniques For CS Applications  Parijat Dube R 7:10-9:10PM CIWW 101
  

  An introduction to theory, computational techniques, and applications of linear algebra, probability and statistics. These three areas of continuous mathematics are critical in many parts of computer science, including machine learning, scientific computing, computer vision, computational biology, natural language processing, and computer graphics. The course teaches a specialized language for mathematical computation, such as Matlab, and discusses how the language can be used for computation and for graphical output. No prior knowledge of linear algebra, probability, or statistics is assumed.

• CSCI-GA.2110-​002
  (5125) Programming Languages Recitation  Anway Agte R 7:10-8:00PM Silv 206 Additional information: Recitation for Section 001
  

  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.2436-​001
  (5185) Realtime and Big Data Analytics  Yang Tang R 4:55-6:55PM CIWW 101
  

  This course introduces architectures and technologies at the foundation of the Big Data movement. These technologies facilitate scalable management and processing of vast quantities of data collected through realtime and near realtime sensing. We explore tools enabling the acquisition of data in the social domain and the fusion of those data when in flight and at rest using Hadoop and Hadoop-related tools.

  The material covered in this course aligns with the prevailing state of the art in Big Data technologies, which continues to be a rapidly evolving landscape as new technologies emerge and existing ones evolve and mature.

  Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by designing, developing, and demonstrating an analytics project of their choosing. Class time will be set aside for project proposal and final demo.

  This is an introductory Hadoop course intended for students who have no experience with the Apache Hadoop ecosystem of tools and frameworks.

• CSCI-GA.2830-​001
  (5176) The Lean Launch Pad*  Yiannis Broustas R 10:15-12:15PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

  Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

  This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands­ on learning on what it’s like to actually start a high­ tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.

• CSCI-GA.2830-​002
  (5177) The Lean Launch Pad Lab*  Yiannis Broustas R 12:30-1:20PM 60 Fifth Ave C15 Additional information: Open only to students in the MS-CEI program
  

  Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

  This course is based on Steve Blank’s Lean LaunchPad curriculum. This course provides real world, hands­ on learning on what it’s like to actually start a high­ tech company. This is a practical class – essentially a lab, not a theory or “book” class. The goal, within the constraints of a classroom and a limited amount of time, is to create an entrepreneurial experience for students with all of the pressures and demands of the real world in an early stage start up. Students will talk to customers, partners, competitors, as they encounter the chaos and uncertainty of how a startup actually works. They will work in teams learning how to turn a great idea into a great company. They will learn how to use a business model to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone would want/use their product. Finally, based on the customer and market feedback gathered, students will use agile development to rapidly iterate their products to build something customers would actually use and buy. Each day will be a new adventure outside the classroom as students test each part of their business model and then share the hard earned knowledge with the rest of the class.

• CSCI-GA.3033-​026
  (5155) Special Topics: Cloud Computing  Jean-Claude Franchitti R 7:10-9:10PM CIWW 312
  

  This course is a capstone course that introduces the latest cloud multi-access computing platform services including support for Internet of Things (IoT), Artificial Intelligence (AI), Web3 decentralized platforms, and other technologies. These services may be leveraged to create industry 4.0 and 5.0 innovative Intelligent Autonomous Networked (IAN) business solutions and deploy them on hybrid multi-cloud platforms so they can operate at the network edge to minimize latency and power consumption. Students will learn about services provided on the top “Big Clouds”, namely Amazon AWS Cloud, Google Cloud, Microsoft Azure Cloud, IBM Cloud, Oracle Cloud, Salesforce, and others. Related services provided on the Cloud and covered in the course include computing and hosting services, storage services, networking services, container management services (e.g., K8s), and shallow/deep machine learning services. Once familiar with Cloud services, Students will learn how to combine them to support cognitive computing and enable the creation of IAN solutions that cater to the practical needs of industry 4.0/5.0 and society 5.0 users in novel and creative ways. As they create these IAN solutions, students will learn about immersive Augmented/Virtual Reality (AR/VR) User Experience and Interfaces (UX/UI), hybrid decentralized computing and DApps, multi-access computing and Internet of Things (IoT), Mobility/5G networking and Software Defined Network (SDN) infrastructures, advanced robotics, and various key aspects of digital security that are critical to ensure the safety of modern software solutions. Throughout the course, students will develop their own Intelligent Autonomous Networked (IAN) business solutions using Cloud-based shallow/deep machine learning and other services and capabilities as noted above. Sample platforms and related application components will be provided as applicable to speed up the development and deployment process.

  
  
  Prerequisites: Programming experience in C/C++, JavaScript, Python, and SQL; basic understanding of assembly language; working knowledge of web application development.
• CSCI-GA.3033-​085
  (5162) Special Topics: Cloud and Machine Learning  Hao Yu R 4:55-6:55PM CIWW 317
  

  This is a capstone course for the MSCS program. This course exposes students to the practical aspects of the state of art in cloud computing with an emphasis on employing cloud for machine learning problems. The course material introduces students to various cloud technologies such as virtualization and container. Students will learn how to build machine learning systems using cloud computing, the application performance characteristics, and develop hands-on experience with programming machine learning applications on the cloud platforms.

  
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Python programming. Students are expected to know basic Linux commands and configurations. Software communication and development tools such as Slack and Github will be used.
• CSCI-GA.3033-​114
  CS-GY 9223-I
  (22062) Special Topics: Recent Developments in Algorithm Design  Anupam Gupta R 2:00-4:30PM CIWW 202
  

  The course covers several recent developments in algorithm design: e.g., recent trends in data structures, fast algorithms for shortest paths, matchings and flows, online and approximation algorithms, stochastic probing and sequencing problems, and algorithms for numerical linear algebra. Each of these topics will be covered in a set of 2-3 lectures by NYU faculty who are specialists in the respective areas.

  
  
  Prerequisites: Graduate level knowledge of algorithms required. For example, Honors Analysis of Algorithms, or similar graduate-level Algorithms courses.
• CSCI-GA.3033-​115
  DS-GA.3001-009
  (23837) Special Topics: Embodied Learning and Vision  Mengye Ren R 4:55-6:35PM 19W4 102
  

  This graduate level course covers advanced topics in embodied visual learning, perception and planning, with applications to robotics and self-driving. Topics include deep learning, computer vision, 3D perception, unsupervised 3D learning, self-supervised representation learning, continual learning, and LLM agents. Students are expected to know basic knowledge of machine learning, deep learning, and computer vision. Students are expected to be proficient in programming in Python. Advanced undergraduate students may enroll upon instructor approval.

• CSCI-GA.3033-​998
  DS-GA.3001-010
  (23838) Special Topics: Embodied Learning and Vision (Recitation)  STAFF R 6:45-7:35PM 19W4 102
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​999
  (5470) Special Topics: Introduction to Computer Vision Recitation  STAFF R 4:55-5:45PM Silv 401 Additional information: Recitation for Section 099
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3850-​004
  (5151) PhD Seminar: Formal Methods  Joseph Tassarotti R 11:00-12:30PM 60 Fifth Ave 446
  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-UA.0002-​002
  (16820) Intro To Computer Programming (No Prior Experience)  Julie Lizardo TR 8:00-9:15AM Online
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students 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-​004
  (16822) Intro To Computer Programming (No Prior Experience)  Waqar Aqeel TR 9:30-10:45AM CIWW 317
  

  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
  (16824) Intro To Computer Programming (No Prior Experience)  Joshua Clayton TR 11:00-12:15PM Silv 411
  

  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-​008
  (16826) Intro To Computer Programming (No Prior Experience)  Emily Zhao TR 12:30-1:45PM Kimmel 808
  

  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-​010
  (16828) Intro To Computer Programming (No Prior Experience)  CANCELLED TR 2:00-3:15PM
  

  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.0003-​001
  (16915) Intro to Computer Programming (Limited Prior Experience)  Adam Meyers TR 11:00-12:15PM Silv 207
  

  Introduces object-oriented programming, recursion, and other important 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: Limited programming experience, or any score on the AP Computer Science examination (students will forfeit the AP credit for scores of 4 or 5), or Data Science for Everyone (DS-UA 111). 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 some 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
  (16834) Intro to Web Design & Computer Principles  Adam Scher TR 8:00-9:15AM Online
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​004
  (16836) Intro to Web Design & Computer Principles  Joshua Clayton TR 3:30-4:45PM Tisch LC9
  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
  (16842) Web Development And Programming  Emily Zhao TR 3:30-4:45PM Silv 401
  

  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-​002
  (16844) Intro To Computer Science  Gabriel Provencher Langlois TR 8:00-9:15AM Silv 208
  

  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-​005
  (16847) Intro To Computer Science  Gizem Kayar TR 11:00-12:15PM CIWW 312
  

  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
  (16850) Intro To Computer Science  Hilbert Gene Locklear TR 2:00-3:15PM CIWW 101
  

  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-​009
  (16851) Intro To Computer Science  Anasse Bari TR 2:00-3:15PM 60 Fifth Ave C10
  

  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-​011
  (16853) Intro To Computer Science  Daniel Zint TR 3:30-4:45PM CIWW 101
  

  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
  (16856) Data Structures  Anasse Bari TR 3:30-4:45PM Silv 405
  

  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-​005
  (16858) Data Structures  Evan Korth TR 12:30-1:45PM Silv 405
  

  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-​009
  (16862) Data Structures  Sungmin Cha TR 2:00-3:15PM CIWW 317
  

  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-​003
  (16876) Computer Systems Organization  Hasan Aljabbouli TR 9:30-10:45AM Online
  

  Covers the internal structure of computers, machine (assembly) language programming, and the use of pointers in high-level languages. Topics include the logical design of computers, computer architecture, the internal representation of data, instruction sets, and addressing logic, as well as pointers, structures, and other features of high-level languages that relate to assembly language. Programming assignments are in both assembly language and other languages.

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0202-​002
  (16883) Operating Systems  Mohamed Zahran TR 11:00-12:15PM 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.0202-​003
  (18081) Operating Systems  Jocelyn Chen TR 12:30-1:45PM CIWW 312
  

  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
  (16886) Basic Algorithms  Alan Siegel TR 2:00-3:15PM Online
  

  Introduction to the study of algorithms. Presents two main themes: designing appropriate data structures and analyzing the efficiency of the algorithms that use them. Algorithms studied include sorting, searching, graph algorithms, and maintaining dynamic data structures. Homework assignments, not necessarily involving programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0330-​001
  MATH-UA.0144
  (16909) Introduction to Computer Simulation  Charles Peskin TR 11:00-12:15PM 194M 203
  

  In this course, students will learn how to do computer simulations of such phenomena as orbits (Kepler problem and N-body problem), epidemic and endemic disease (including evolution in response to the selective pressure of a malaria), musical stringed instruments (piano, guitar, and violin), and traffic flow in a city (with lights, breakdowns, and gridlock at corners). The simulations are based on mathematical models, numerical methods, and Matlab programming techniques that will be taught in class. The use of animations (and sound where appropriate) to present the results of simulations will be emphasized.

  
  
  Prerequisites: Calculus I (MATH-UA 121) or Math for Economics II (MATH-UA 212) and General Physics (PHYS-UA 11).
• CSCI-UA.0381-​001
  (16917) Programming Tools for the Data Scientist  Hasan Aljabbouli TR 3:30-4:45PM 60 Fifth Ave 150
  

  Lecture and laboratory.  Building applications in Python using a project-based learning approach. Students use Python packages in a variety of applied areas, such as textual analysis and data visualization. Does not count toward the computer science major. 

  
  
  Prerequisites: Data Science for Everyone (DS-UA 111) or equivalent proficiency in Python, and either Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3).
• CSCI-UA.0421-​001
  (16895) Numerical Computing   Michael Overton TR 11:00-12:15PM CIWW 101
  

  The need for floating-point arithmetic, the IEEE floating-point standard, and the importance of numerical computing in a wide variety of scientific applications. Fundamental types of numerical algorithms: direct methods (e.g., for systems of linear equations), iterative methods (e.g., for a nonlinear equation), and discretization methods (e.g., for a differential equation). Numerical errors: can you trust your answers? Uses graphics and software packages such as Matlab. Programming assignments.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201), either Calculus I (MATH-UA 121) or both of Mathematics for Economics I and II (MATH-UA 211 and 212), and Linear Algebra (MATH-UA 140), or permission of instructor.
• CSCI-UA.0453-​001
  (16896) Theory Of Computation   Richard Cole TR 9:30-10:45AM CIWW 312
  

  A mathematical approach to studying topics in computer science, such as regular languages and some of their representations (deterministic finite automata, nondeterministic finite automata, regular expressions) and proof of nonregularity. Context-free languages and pushdown automata; proofs that languages are not context-free. Elements of computability theory. Brief introduction to NP-completeness.

  
  
  Prerequisites: Basic Algorithms (CSCI-UA 310) and Computer Systems Organization (CSCI-UA.201).
• CSCI-UA.0467-​001
  (16918) Applied Internet Technology  Joseph Versoza TR 9:30-10:45AM CIWW 101
  

  Applied Internet Technology is a practical introduction to creating modern web applications. It covers full-stack (that is, every aspect of building a database driven web application: server programming, database implementation, frontend markup, styling and interactivity) web development. It includes topics such as database and data model design, web application architecture, separation of logic and presentation, handling user input and processing form data, managing asynchronous processes, strategies for creating real-time web applications, and handling client-side interactivity. Students will use current server and client-side web frameworks and libraries to build dynamic, data-driven sites. Various applications to support development will also be introduced, such as version control, static analysis tools, and build systems.

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201). Students who complete Applied Internet Technology (CSCI-UA 467) with a C or better are not eligible to take Web Development and Programming (CSCI-UA 61).
• CSCI-UA.0469-​001
  (16923) Natural Language Processing  Adam Meyers TR 8:00-9:15AM 12WV G08
  

  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: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0474-​001
  (16899) Software Engineering  Amos Bloomberg TR 2:00-3:15PM Online
  

  An intense hands-on study of practical techniques and methods of software engineering. Topics include advanced object-oriented design, design patterns, refactoring, code optimization, universal modeling language, threading, user interface design, enterprise application development, and development tools. All topics are integrated and applied during the semester long group project. The aim of the project is to prepare students for dynamics in a real workplace. Members of the group meet on a regular basis to discuss the project and to assign individual tasks. Students are judged primarily on the final project presentations. Not offered every academic year. 

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0479-​002
  (16921) Data Management and Analysis  Matthew Zeidenberg TR 11:00-12:15PM Kimmel 808
  

  Extracting, transforming and analyzing data in myriad formats. Using traditional relational
  databases as well as non-relational databases as well as non-relational databases to store, manipulate, and query data. Students write custom programs, create queries, and use data analysis tools and libraries on a wide array of data sets.  Additional topics: data modeling, cloud databases and AP programming.

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0480-​041
  (16902) Special Topics: Computer Graphics  Gizem Kayar TR 12:30-1:45PM CIWW 101
  

  This course provides an introduction to the field of Computer Graphics. We will cover the basic mathematical concepts, such as 2D and 3D transformations, study the interaction of light with geometry to derive shading models, and implement rendering algorithms such as raytracing and rasterization. We will investigate how these fundamental components are integrated into current graphics processors and study the corresponding programming APIs.

  
  
  Prerequisites: CSCI-UA 201 Computer Systems Organization, CSCI-UA-310 (Basic Algorithms) and MATH-UA-140 (Linear Algebra), or departmental consent.
• CSCI-UA.0480-​051
  (16904) Special Topics: Parallel Computing  Mohamed Zahran TR 2:00-3:15PM Online
  

  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: Computer System Organization (CSCI-UA.0201) with a grade of C or better
• CSCI-UA.0480-​075
  (23956) Special Topics: Introduction to Deep Learning  Alfredo Canziani TR 12:30-1:45PM CIWW 202
  

  This course provides a comprehensive introduction to deep learning, exploring fundamental architectures, theoretical foundations, and practical applications of neural network models. Students will develop a rigorous understanding of modern deep learning techniques, from classic perceptron models to cutting-edge transformer architectures.

  
  Learning Objectives
  By the end of this course, students will be able to:
   - Understand the mathematical foundations of neural network learning
   - Design, implement, and train various neural network architectures
   - Critically evaluate deep learning model performance and limitations
   - Apply deep learning techniques to real-world problems across domains

  
  
  Prerequisites: MATH-U 121, MATH-UA 140 & CSCI-UA 201

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  Indicates controlled enrollment (permission number required for registration). Contact Santiago Pizzini (pizzini@cs.nyu.edu).
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