Course Schedule

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

View undergraduate course grid View graduate course grid

Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-004
(5118) Fundamental Algorithms Yevgeniy Dodis T 4:55-6:55PM CIWW 312

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
(5134) Operating Systems Yang Tang T 4:55-6:55PM CIWW 101

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2437-001
(5186) Big Data Application Development Yang Tang T 7:10-9:10PM CIWW 101

This is an introductory course on Spark programming, Spark architecture, Spark SQL, Spark Streaming, and integration of Spark with the Hadoop ecosystem for developing Big Data analytics applications. The course project can be completed with Scala or Python, and Spark. This course covers technologies that integrate well with Spark in the creation of Big Data analytics applications.

Students are required to complete weekly reading and programming assignments and demonstrate mastery of course topics by developing a final project using Spark.

Prerequisites: Prerequisites include experience with Hadoop, which commonly comes from having completed the Realtime and Big Data Analytics course. There will be a review of Apache Hadoop, but students should have experience with Hadoop MapReduce and HDFS.
CSCI-GA.2569-002
DS-GA.1005-002
(5485) Inference and Representation Lab STAFF T 1:30-2:20PM Silv 520 Additional information: Lab for Section 001

This course covers graphical models, causal inference, and advanced topics in statistical machine learning.

Prerequisites: DS-GA 1004
CSCI-GA.2820-001
(5172) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2820-002
(5173) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Additional information: Open only to students in the Stern TECH MBA program

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.3033-052
(5157) Special Topics: Advanced Machine Learning Mehryar Mohri T 4:55-6:55PM CIWW 317

This course discusses advanced topics in theoretical machine learning, extending beyond the scope of foundational graduate courses. The primary goal is to introduce key concepts not covered in basic ML courses such as Foundations of Machine Learning, while also exploring cutting-edge learning problems that can serve as a springboard for research or the development of novel application-relevant techniques.

A central focus of the course is a rigorous analysis of the rich field of online learning. In addition, the material encompasses a broad range of advanced topics in supervised learning, providing a comprehensive overview of the theoretical underpinnings of modern machine learning methods.

Through a combination of lectures, discussions, and assignments, students will gain a deep understanding of the fundamental principles governing learning algorithms. The course will equip students with the necessary theoretical foundations to conduct cutting-edge research in theoretical machine learning, as well as to develop novel and effective machine learning solutions for real-world problems.
CSCI-GA.3033-077
(5161) Special Topics: Big Data and ML Systems Lakshminarayanan Subramanian T 10:15-12:15PM 60 Fifth Ave C15

This class will discuss recent research on machine learning systems, esp. those targeted at accelerating deep learning workloads. We will take a deep dive exploring how these systems work so that ML models can be written in a high-level language and executed as low-level kernels on parallel hardware accelerators. Topics covered in this course include: basics of neural networks, how they are programmed and executed by today's deep learning frameworks, automatic differentiation, deep learning accelerators, distributed training techniques, computation graph optimizations, automated kernel generation.

The class will require reading research papers, presenting papers, working on programming labs, and completing a significant final project.

This is a capstone course for the MSCS program.

Prerequisites: The class does require an undergraduate-level knowledge in operating systems, programming languages, algorithms, linear algebra, probability and statistics. If you are new to these concepts, then this class may be advanced for you.
CSCI-GA.3033-097
(5164) Special Topics: Virtual Reality* Kenneth Perlin T 4:55-6:55PM 60 Fifth Ave 446

Students will learn how to create shared interactive experiences in Virtual Reality (VR), using an open-source WebXR platform. This is a hands-on projects based course. Topics include design and implementation of interactive tools for collaborative modeling and animation, with a focus on graphical user interface (GUI) design for VR.

Apply for a seat in the course: https://forms.gle/ak6XsSX32XvkszsW8

Prerequisites: CSCI-GA.2270 Computer Graphics or CSCI-UA.0480-041 Computer Graphics. Permission of the instructor required.
CSCI-GA.3033-106
Math-GA.2840-003
(5167) Special Topics: Geometric Methods in Algorithm Design Subhash Khot T 10:15-12:15PM CIWW 202

For many NP-hard problems, one can efficiently compute approximate solutions with a non-trivial guarantee on the approximation ratio. Starting with the seminal work of Goemans and Williamson in early 90s, several approximation algorithms have been designed based on a semi-definite programming relaxation followed by a rounding scheme. This course will cover many of these SDP-based algorithms along with other applications of SDPs in computer science. The course will also cover hardness of approximation results, showing that for several NP-hard problems, it remains NP-hard to compute approximations beyond a certain threshold.
CSCI-GA.3033-109
(5169) Special Topics: Computer Vision for Science and Engineering David Fouhey T 4:55-6:55PM CIWW 202

One of the most important emerging areas for computer vision and machine learning is in helping the sciences and engineering extract more and better information from their data. This course will explore the applications of computer vision in the sciences and engineering, including some of the challenges that are not normally seen in "standard" machine learning and vision. Classes will be a mix of lectures, discussion of recent work by students, and hands-on group projects at the intersection of ML and science.

Prerequisites: Students should come with familiarity with computer vision or machine learning as well as an enthusiasm for trying new data but no formal prerequisites are required.
CSCI-GA.3850-001
(5150) PhD Seminar: Cryptography ** Yevgeniy Dodis T 12:30-3:15PM CIWW 412
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

*

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.

Classroom Locations

Notice: This webpage is subject to change and will be revised and updated as necessary.