Course Schedule

Current semester Spring 2027, and future semesters are now available to view.

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-001
DS-GA.1170-001
(4336) Fundamental Algorithms Yevgeniy Dodis W 4:55-6:55PM CIWW 109

Course Description:

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

Required Foundational course for MSCS, MSIS

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.1170-002
DS-GA.1170-002
(4337) Fundamental Algorithms Recitation TBA R 5:55-6:45PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

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

Required Foundational course for MSCS, MSIS

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.1170-003
DS-GA.1170-002
(4338) Fundamental Algorithms Recitation TBA F 4:55-5:45PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

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

Required Foundational course for MSCS, MSIS

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.1170-004
(4339) Fundamental Algorithms Yevgeniy Dodis T 4:55-6:55PM CIWW 312 Additional information: Recitation for Section 003

Course Description:

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

Required Foundational course for MSCS, MSIS

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
(4340) Fundamental Algorithms Recitation TBA R 3:45-4:35PM GCASL 275 Additional information: Recitation for Section 004

Course Description:

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

Required Foundational course for MSCS, MSIS

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
(4341) Mathematical Techniques For CS Applications Parijat Dube R 7:10-9:10PM CIWW 317

Course Description:

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.2110-001
(4342) Programming Languages Cory Plock M 4:55-6:55PM CIWW 109

Course Description:

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-002
(4343) Programming Languages Recitation TBA R 7:10-8:00PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

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

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-003
(4344) Programming Languages Recitation TBA F 11:15-12:05PM Silv 206 Additional information: Recitation for Section 001

Course Description:

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.2130-001
(4345) Compiler Construction Sam Westrick M 10:15-12:15PM 60 Fifth Ave C15

Course Description:
This is a capstone course based on compilers and modern programming languages. The topics covered include structure of one-pass and multiple-pass compilers; symbol table management; lexical analysis; traditional and automated parsing techniques, including recursive descent and LR parsing; syntax-directed translation and semantic analysis; run-time storage management; intermediate code generation; introduction to optimization; and code generation. The course includes a special compiler-related capstone project, which ties together concepts of algorithms, theory (formal languages), programming languages, software engineering, computer architecture, and other subjects covered in the MS curriculum. This project requires a substantial semester-long programming effort, such as construction of a language compilation or translation system that includes lexical and syntactic analyzers, a type checker, and a code generator.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2250-001
(4346) Operating Systems Hubertus Franke M 2:45-4:45PM GCASL 288

Course Description:

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.2250-002
(4347) Operating Systems Mohamed Zahran T 4:55-6:55PM Online

Course Description:

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.2262-001
(4348) Data Communications and Networks Jean-Claude Franchitti M 7:10-9:10PM 60 Fifth Ave 150

Course Description:

This course teaches the design and implementation techniques essential for engineering robust networks. Topics include networking principles, Transmission Control Protocol/Internet Protocol, naming and addressing (Domain Name System), data encoding/decoding techniques, link layer protocols, routing protocols, transport layer services, congestion control, quality of service, network services, programmable routers and overlay networks.

Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
CSCI-GA.2390-001
(23145) Logic In Computer Science Ernest Davis T 4:55-6:55PM CIWW 317

Course Description:

Introduction to formal logic: mathematical and computational issues. Topics to be covered include: Propositional logic: Foundations. Axiomatization. Completeness of propositional resolution. Davis-Putnam algorithm. SAT-solving technology.

First-order logic: Axiomatization. Tarskian semantics. First-order resolution. Completeness of resolution. Semi-decidability of inference. First-order logic with equality. Sorted first-order logic. Horn-clause inference.

Modal logic. Dynamic logic. Applications of temporal logics to program language semantics and program verification. Possible-world (Kripkean) semantics. Axiomatizations of propositional modal logics and their completeness proofs.

History of modern (1840-present) formal logic and formalized mathematical proof.

Prerequisites:
Fundamental Algorithms or equivalent.
CSCI-GA.2421-001
MATH-GA 2020-001
(4495) Numerical Methods II Hanwen Zhang MW 11:00-12:15PM CIWW 1302

Course Description:

This course will cover fundamental methods that are essential for the numerical solution of differential equations. It is intended for students familiar with ODE and PDE and interested in numerical computing; computer programming assignments form an essential part of the course. The course will introduce students to numerical methods for (1) nonlinear equations, Newton's method; (2) ordinary differential equations, Runge-Kutta and multistep methods, convergence and stability; (3) finite difference, finite element and integral equation methods for elliptic partial differential equations; (4) fast solvers, multigrid methods; and (5) parabolic and hyperbolic partial differential equations.

Text: LeVeque, R. (2007). Classics in Applied Mathematics [Series]. Finite Difference Methods for Ordinary and Partial Differential Equations. Philadelphia, PA: Society for Industrial and Applied Mathematics

Prerequisites: Numerical linear algebra, elements of ODE and PDE.
CSCI-GA.2433-001
DS-GA.2433-001
(4350) Database Systems Jean-Claude Franchitti R 4:55-6:55PM GCASL 279

Course Description:
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.2436-001
(4399) Realtime and Big Data Analytics Yang Tang R 4:55-6:55PM CIWW 101

Course Description:

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.2437-001
(4400) Big Data Application Development Yang Tang T 7:10-9:10PM CIWW 101

Course Description:

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: Students are required to successfully complete CSCI-GA 2436 Realtime and Big Data Analytics prior to taking this course. Students should have experience with Hadoop MapReduce and HDFS.
CSCI-GA.2565-001
(4352) Machine Learning Rajesh Ranganath W 4:55-6:55PM Silv 408

Course Description:

This course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. The course covers the mathematical methods and theoretical aspects but primarily focuses on algorithmic and practical issues.

Prerequisites:
Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics, CSCI-GA.1180 Mathematical Techniques for CS Applications.
CSCI-GA.2572-001
DS-GA.1008-001
(4389) Deep Learning Yann LeCun
Alfredo Canziani M 12:20-2:20PM 19UP 102

Course Description:

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2572-002
DS-GA.1008-002
(4390) Deep Learning Lab Alfredo Canziani M 2:30-3:20PM 19UP 102 Additional information: Lab for Section 001

Course Description:

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2590-001
(4353) Natural Language Processing Haneul Yoo W 4:55-6:55PM Silv 207

Course Description:

How can we teach machines to understand human languages so that they can answer queries, summarize dense information, or hold a conversation with us? The primary goal of this course is to provide students with the principles and state-of-the-art tools needed to solve a variety of NLP problems. We will focus on three paradigms in the field of NLP: supervised learning, pretrain-then-finetune, and the most recent large language models, with a focus on representation learning techniques. Students are expected to read research papers and gain hands-on experience through coding assignments.

Prerequisites:
Probability, statistics, linear algebra (DS-GA.1002, MATH-UA.140, MATH-UA.235); Algorithms and data structure (CSCI-UA.102); Basic knowledge in machine learning (DS-GA.1003, CSCI-UA.0473). We will not spend a significant amount of time on machine learning basics so some prior exposure to the supervised learning framework (e.g., loss functions, SGD) is expected. Good comfort level with Python
CSCI-GA.2620-001
(4354) Networks and Mobile Systems Lakshminarayanan Subramanian T 10:15-12:15PM 60 Fifth Ave 250

Course Description:

This is a capstone course. A course in computer networks and large-scale distributed systems. Teaches the design and implementation techniques essential for engineering both robust networks and Internet-scale distributed systems. The goal is to guide students so they can initiate and critique research ideas in networks and distributed systems and implement and evaluate a working system that can handle a real-world workload. Topics include routing protocols, network congestion control, wireless networking, peer-to-peer systems, overlay networks and applications, distributed storage systems, and network security.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2820-001
(4391) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online

Course Description:

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
(4392) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Additional information: Lab for Section 001.

Course Description:

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.2820-003
(4393) DevOps and Agile Methodologies John Rofrano W 4:55-6:55PM Online

Course Description:

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.2820-004
(4394) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM Online Additional information: Lab for Section 003

Course Description:

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.2830-001
(4395) The Lean Launch Pad* Yiannis Broustas R 10:15-12:15PM 60 Fifth Ave 125 Additional information: Open only to students in the MS-CEI program.

Course Description:

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
(4396) The Lean Launch Pad Lab* Yiannis Broustas R 12:30-1:20PM 60 Fifth Ave 125 Additional information: Open only to students in the MS-CEI program.

Course Description:

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.2945-002
MATH-GA 2012-002
(4496) Adv Topics Num Analysis: High Performance Numerical Computation Georg Stadler
Florian Schaefer TR 2:00-3:15PM CIWW 1302

Course Description:
This class will be an introduction to the fundamentals of high-performance numerical computation. We will establish a basic understanding of modern computer architectures (CPUs and accelerators, memory hierarchies, interconnects) and of approaches to programming these machines: single instruction-multiple data (SIMD), shared and distributed memory parallelism. Issues such as load balancing, communication, memory locality and synchronization will be covered and illustrated in the context of numerical algorithms. Along the way you will be exposed to important tools for high performance computing such as profilers, schedulers, and version control systems. This will be a hands-on class, with several computing assignments and a larger project.

Prerequisites: Prerequisites for the course are (serial) programming experience with C/C++ (I will use C in class) or FORTRAN, and some familiarity with numerical methods.
CSCI-GA.3033-026
(4372) Special Topics: Agentic Cloud Computing Jean-Claude Franchitti R 7:10-9:10PM CIWW 312

Course Description:

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.3033-034
(4373) Special Topics: Multicore Processors: Architecture & Programming Mohamed Zahran W 12:30-2:30PM Online

Course Description:

This course serves as a capstone for the MSCS program.

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.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-059
(4374) Special Topics: Big Data Science Anasse Bari R 4:55-6:55PM CIWW 317

Course Description:

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.3033-099
DS-GA 3001-009
(4403) Special Topics in DS: Introduction to Computer Vision Jean Ponce M 2:45-4:45PM Silv 401

Course Description:

This course aims to cover broad topics in computer vision, and is not primarily a deep learning course. We will cover topics in traditional computer vision such as camera geometry, image formation, segmentation, object recognition, classification, and detection.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-115
DS-GA.3001-003
(4404) Special Topics: Embodied Learning and Vision Mengye Ren R 2:45-4:45PM 12WP L113

Course Description:

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.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-123
(6950) Special Topics: Encrypted Computation Nir Bitansky W 10:15-12:15PM 60 Fifth Ave C15

Course Description:

The modern computing environment is very different than it used to be. Sensitive information that once existed only on local trusted machines—medical records, personal preferences, and our finances–is now stored and processed by various entities onew the web, who we often do not trust. To maintain privacy in this reality, a new array of cryptographic tools has been developed over the past decade and a half allowing different forms of encrypted computation. This includes: fully-homomorphic encryption, attribute-based encryption, functional encryption, and program obfuscation. The course will cover some of these advances with special focus on the main open questions in the area.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-129
(23122) Special Topics: Artificial Intelligence in Genomics Nadav Brandes R 12:00-2:30PM MSB G19 Additional information: Location will vary. Please contact the instructor.

Course Description:

The course explores the practical applications of AI in genomics and genetics. Students will learn how AI can integrate diverse biological data – from evolutionary patterns across species to clinical records – to interpret our genome and help diagnose medical conditions, predict disease risk, and engineer new therapeutic proteins. A major focus of the course will be hands on work with modern AI techniques that address real world applications. This includes training models from scratch, as well as applying and fine-tuning existing models. Key machine-learning and AI concepts will be thoroughly covered, but we will largely avoid the mathematical theory taught in other machine learning courses. Our emphasis will be on implementation of these powerful models to advance biomedicine.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-130
CS-GY.9223-J
(4379) Special Topics: Randomness and Computation Sanjeev Khanna T 2:45-4:45PM CIWW 202

Course Description:

Can you determine whether two very large documents are identical by examining only a handful of bits? Can you verify the output of a program without redoing the entire computation? Can you be convinced of the correctness of a mathematical proof by checking only a few random bits? Can you properly color a graph without ever seeing all of its edges? Surprisingly, the answer to all these questions is yes — provided we allow the use of randomization.

Randomness has emerged as a fundamental resource in computation, enabling algorithms that are often simpler, faster, or more space-efficient than their deterministic counterparts. This course will explore the design and analysis of randomized algorithms, as well as their inherent limitations, studied through query complexity and communication complexity. Topics include sublinear algorithms, approximation algorithms and hardness of approximation, algebraic fingerprinting techniques, interactive proofs and the PCP theorem, random walks and Markov chains with applications to bounded-space computation, and the complexity classes that capture randomized computation, including BPP, RP, and ZPP.

The course is suitable for graduate students and advanced undergraduates, with a strong preparation in undergraduate algorithms (for instance, CSCI-GA.3520-001 or equivalent).

Note: The topics in this course are largely disjoint from those in Prof. Anupam Gupta’s Fall 2025 class on Randomized Algorithms. This course is suitable both for students who have not taken Prof. Gupta’s class and for those who have, and wish to continue exploring randomized algorithms.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-131
(23858) Special Topics: Building LLM Reasoners Sherry Yang M 4:55-6:55PM Online

Course Description:

Large language models are a foundational building block of generative AI systems. Modern LLMs go far beyond the capabilities of early LLMs like GPT-3 by demonstrating the capacity for reasoning in natural language. This course provides a hands-on crash course into what makes these models work, covering (1) fundamental building blocks like tokenizers and optimizers; (2) optimizations such as FlashAttention; (3) approaches for learning reasoners such as supervised fine-tuning, reinforcement learning, and inference-time methods. Assignments will focus on implementation of these approaches.

Prerequisites: CSCI-GA.2590 Natural Language Processing or equivalent; CSCI-GA-2565 Machine Learning or equivalent; Deep learning or equivalent; Python programming experience; Experience with concepts from probability, linear algebra
CSCI-GA.3033-143
(23513) Special Topics: Four Decades at the Edge of Computation, Biology, and Mind Bhubaneswar Mishra F 2:45-4:45PM CIWW 109

Course Description:

This graduate course is a scholarly retrospective in the spirit of CMU's celebrated "Last Lectures" tradition — a wide-ranging tour through the research themes Professor Mishra has pursued at NYU over nearly four decades. It is offered as his final course before retirement, and is intended for advanced graduate students willing to think across disciplinary boundaries, and transfer the innovations outside academia.

The premise is simple but ambitious: seemingly disparate fields — from the genetic code to smart contracts, from humanoid grasping to the P vs NP question — share deep structural, logical, and philosophical underpinnings. Each week we will surface one of those underpinnings and follow it across domains, weaving together mathematics, theoretical computer science, computational biology, artificial intelligence, control theory, and game theory.

Topics will include: aging, neuro-degeneration, and cancer as problems of genomic instability and systems biology; the game theory of endosymbiosis and its echoes in society and human-AI co-evolution; human–robot endosymbiosis, grasping, and obstacle avoidance; signaling games in finance, smart contracts, and stable coins; logics, languages, and heuristics for theorem proving and formal verification; evolution, stability, and control theory; foundations and frontiers of machine learning, including world models; graph properties, communities, and efficient algorithms; and the enduring mystery of P and NP.

Prerequisites: Graduate standing or instructor permission. Open to students across Courant, NYU CS, and allied departments. A public-facing lecture component, with guest discussants, is anticipated.
CSCI-GA.3033-144
(4381) Special Topics: Real-time Robot Planning Algorithms and Analysis Benjamin Riviere M 12:30-2:30PM 60 Fifth Ave C10

Course Description:

This course studies the mathematical analysis and practical implementation of robot planning algorithms. Students will develop a theoretical toolkit and use it to analyze when, why, and how fast algorithms converge across topics including trajectory optimization, reinforcement learning, motion planning, search, and multi-agent cooperation. A recurring theme is the interplay between theoretical guarantees and real-world deployment, including how convergence properties degrade under noise, model mismatch, and sim-to-real transfer. The course material will include lectures and seminars.

Prerequisites: Linear algebra, probability, and calculus. No prior experience in robotics is necessary.
CSCI-GA.3033-145
(4382) Special Topics: Program Verification Thibault Dardinier W 12:30-2:30PM 194M 305

Course Description:

How can we be certain that a program is correct? Testing can show the presence of bugs, but never their absence. Automated deductive verifiers, such as Dafny, Verus, or Viper, offer an alternative: programmers annotate their code with specifications, and the verifier attempts to prove, largely automatically, that all executions satisfy the specifications. Automated verifiers are used in industry to verify critical software, and they are becoming increasingly important as more code is written by AI.

This course teaches both how to use such verifiers and how they work under the hood. Students will learn to write specifications and verify programs with state-of-the-art verifiers, and to understand exactly what a successful verification guarantees and under which assumptions. We will study the semantic and logical foundations of program verification and the techniques used to automate reasoning about programs. We will focus on imperative programs, including programs that manipulate the heap or use concurrency, and on properties beyond functional correctness, such as information-flow security.

Prerequisites: Programming experience and familiarity with basic logic and mathematical proofs. No prior background in programming language theory or verification is required.
CSCI-GA.3033-146
(4383) Special Topics: Data Science for Wearables and mHealth Souptik Barua MR 3:00-4:30PM MSB 1220 Additional information: Location will vary. Please contact the instructor.

Course Description:

Patient-generated health data (PGHD) collected via wearable sensors, continuous monitors, and mobile health (mHealth) applications represents one of the fastest-growing data modalities in healthcare. However, translating high-frequency, noisy sensor streams into clinically meaningful digital biomarkers requires specialized analytical methods distinct from traditional electronic health record (EHR) analytics.

This proposed upper-level elective bridges that gap for students in the MS in Biomedica Informatics program. By examining the end-to-end data pipeline— from physical sensor mechanics and signal processing to statistical modeling and multi-modal integration— this course prepares students to work with continuous biometric data in clinical research, digital health product development, and health systems informatics.

The curriculum is built around high-impact, real-world data modalities including continuous glucose monitoring (CGM), tri-axial actigraphy, photoplethysmography (PPG), digital food logs, and ecological momentary assessment (EMA).

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-147
(4384) Special Topics: Advanced Algorithms Anupam Gupta W 2:45-5:15PM CIWW 202

Course Description:

This is an intensive graduate course on the design and analysis of algorithms. This course is primarily aimed at graduate and advanced undergraduate students interested in doing research in theoretical computer science, and the goal is to expose these students to many of the ideas being used in algorithms research today. It is more specialized and in-depth than the graduate and undergraduate level Algorithms courses being offered: we will cover a broad set of advanced algorithmic ideas (some classical, some very recent), and the theory behind it (theorems, proofs). The goal is to cover advanced content, proofs, and new techniques --- topics include (near)-linear time algorithms for spanning trees, shortest paths, and matchings, linear and convex optimization, continuous methods for discrete optimization, online learning and the connection to measure concentration. A major focus will be on the use of linear/convex programming duality, and how it arises in many contexts in algorithm design. Previous versions of this course can be found linked from the instructor's webpage.

Prerequisites: We expect students to have a good background in algorithm design. For example, Honors Analysis of Algorithms (CSCI-GA.3520-001), Algorithms 2 (CS-GY 6043), Algorithmic Machine Learning and Data Science (CS-GY 6763), or similar graduate-level Algorithms courses. Solid understanding of probability, linear algebra, and calculus, and mathematical maturity is a must.
CSCI-GA.3033-934
(4385) Special Topics: Multicore Processors: Architecture & Programming Recitation TBA F 12:30-1:20PM 60 Fifth Ave 150 Additional information: Recitation for Section 034

Course Description:

This is the recitation for section 034. Refer to the lecture for description.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-998
DS-GA.3001-010
(4405) Special Topics: Embodied Learning and Vision (Recitation) STAFF R 4:55-5:45PM Bobst LL138 Additional information: Recitation for Section 115

Course Description:

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-999
(4406) Special Topics: Intro to Computer Vision Recitation STAFF W 11:15-12:05PM 60 Fifth Ave 150 Additional information: Recitation for Section 099

Course Description:

Topics vary each semester.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3140-001
(4398) Abstract Interpretation Patrick Cousot M 4:55-6:55PM 60 Fifth Ave 161

Course Description:

Many numerical or symbolic problems are very hard or even undecidable meaning that no computer algorithm can solve them exactly in reasonable time and memory space. This course introduces the latest techniques in abstract interpretation, a powerful framework for automatically calculating approximate solutions of such difficult problems. The main emphasis will be on software verification and automatic analysis with a wide range of applications from compilers to social networks.

Prerequisites: Some facility with logic, sets, linear algebra, graphs, formal languages, mathematical proofs, and programming languages is expected.
CSCI-GA.3205-001
(4355) Applied Cryptography & Network Security Mazdak Zamani W 4:55-6:55PM 60 Fifth Ave C10

Course Description:
This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.
CSCI-GA.3812-001
(4386) Innovative Technology Projects * Evan Korth W 5:00-8:00PM CIWW 312

Course Description:

This is a capstone course that connects students directly with real-world technology problems. The goal of this course is to teach the skills needed for success in real-world 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.3813-001
(4356) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-002
(4357) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-003
(4358) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-004
(4359) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-005
(4360) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-006
(4361) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-007
(4362) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-008
(4363) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3840-001
(4364) Master's Thesis Research * STAFF -

Course Description:

Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
CSCI-GA.3850-001
(4365) PhD Seminar: Cryptography ** Yevgeniy Dodis T 11:00-1:50PM CIWW 312

Course Description:
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-GA.3850-002
(4366) PhD Seminar: Theory Anupam Gupta R 11:00-1:50PM CIWW 312

Course Description:
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-GA.3850-004
(4367) PhD Seminar: Formal Methods Sam Westrick W 11:00-12:30PM 60 Fifth Ave 446

Course Description:
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-GA.3860-001
(4368) PhD Thesis Research ** STAFF -

Course Description:

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(4369) Internship In Computer Science MS* STAFF -

Course Description:

Participation in a programming project or research project conducted outside of the classroom in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may take this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.

Section 1 is for Master's students. Section 2 is for Ph.D. students.

Prerequisites: Permission of Director of Graduate Studies.
CSCI-GA.3870-002
(4370) Internship in Computer Science PhD* STAFF -

Course Description:

Participation in a programming project or research project conducted outside of the classroom in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may take this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.

Section 1 is for Master's students. Section 2 is for Ph.D. students.

Prerequisites: Permission of Director of Graduate Studies.
CSCI-UA.0001-001
(19095) Computers & Society: Understanding AI: Technology Transforming Society Alfredo Canziani TR 3:30-4:45PM GCASL C95

Course Description:

Addresses the impact of the digital computer on individuals, organizations, and modern society as a whole, and the social, political, and ethical issues involved in the computer industry. Topics change to reflect changes in technology and current events. Features guest lecturers from various fields. Not offered every academic year.

Prerequisites: No prior computing experience is assumed. Not intended for computer science majors.
CSCI-UA.0001-002
(22995) Computers and Society: Introduction to Computer Programming with AI Assistance Matthew Zeidenberg MW 2:00-3:15PM

Course Description:

Addresses the impact of the digital computer on individuals, organizations, and modern society as a whole, and the social, political, and ethical issues involved in the computer industry. Topics change to reflect changes in technology and current events. Features guest lecturers from various fields. Not offered every academic year.

Prerequisites: No prior computing experience is assumed. Not intended for computer science majors.
CSCI-UA.0002-001 Intro To Computer Programming (No Prior Experience) Khye Borg Liew MW 8:00-9:15AM Online

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-002
(8863) Intro To Computer Programming (No Prior Experience) Craig Kapp F 9:30-12:00PM 19W4 101

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-003
(8864) Intro To Computer Programming (No Prior Experience) Mihir Patil TR 9:30-10:45AM GCASL 288

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-004
(8865) Intro To Computer Programming (No Prior Experience) Jiri Motejlek MW 11:00-12:15PM CIWW 202

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-005
(8866) Intro To Computer Programming (No Prior Experience) Joshua Clayton TR 11:00-12:15PM Tisch LC11

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-006
(8867) Intro To Computer Programming (No Prior Experience) Karl Rosenberg MW 12:30-1:45PM CIWW 317

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-007
(8868) Intro To Computer Programming (No Prior Experience) Khye Borg Liew MW 2:00-3:15PM Online

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-009
(19310) Intro To Computer Programming (No Prior Experience) Amanda Steigman TR 8:00-9:15AM 12WV L120

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0002-010
(8905) Intro To Computer Programming (No Prior Experience) Michal Sadowski MW 9:30-10:45AM 7E12 LL23

Course Description:

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

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
CSCI-UA.0003-001
(8939) Intro to Computer Programming (Limited Prior Experience) Karl Rosenberg TR 11:00-12:15PM CIWW 317

Course Description:

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.0004-001
(8870) Intro to Web Design & Computer Principles Alvaro Olsen MW 8:00-9:15AM CIWW 101

Course Description:
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0004-002
(8871) Intro to Web Design & Computer Principles Adam Scher TR 8:00-9:15AM Online

Course Description:
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0004-003
(8872) Intro to Web Design & Computer Principles Mark Ramos MW 11:00-12:15PM Silv 208

Course Description:
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0004-004
(8873) Intro to Web Design & Computer Principles Joshua Clayton TR 3:30-4:45PM Silv 208

Course Description:
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0004-005
(8874) Intro to Web Design & Computer Principles Mark Ramos TR 11:00-12:15PM CIWW 202

Course Description:
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0004-006
(8875) Intro to Web Design & Computer Principles Alvaro Olsen MW 3:30-4:45PM 194M 206

Course Description:
Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.

Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
CSCI-UA.0060-001
(8877) Database Design And Implementation Mark Ramos TR 2:00-3:15PM Silv 411

Course Description:

Introduces principles and applications of database design and working with data. Students use Python as they prepare, analyze, and work with data and use SQL to study the principles and implementations of relational databases. Also covers other database paradigms, such as NoSQL. Students apply these principles to computer systems and their respective fields of interest.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Students that have successfully completed Data Management and Analysis (CSCI-UA 479) are NOT eligible to take this course.
CSCI-UA.0061-001
(8878) Web Development And Programming Craig Kapp F 2:00-4:30PM GCASL 279

Course Description:

Provides a practical approach to web technologies and programming. Students build interactive, secure, and powerful web programs. Covers client and server side technologies for the web.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), and Introduction to Web Design and Computer Principles (CSCI-UA 4). Students that have successfully completed Applied Internet Technology (CSCI-UA 467) are NOT eligible to take this course.
CSCI-UA.0101-001
(8879) Intro To Computer Science STAFF MW 9:30-10:45AM 60 Fifth Ave 110

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-002
(8880) Intro To Computer Science Nader Nassar TR 9:30-10:45AM GCASL 383

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-003
(8881) Intro To Computer Science Hilbert Gene Locklear MW 12:30-1:45PM Silv 411

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-004
(8876) Intro To Computer Science Candido Cabo MW 2:00-3:15PM GCASL 383

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-005
(8882) Intro To Computer Science Anasse Bari F 2:00-4:30PM CIWW 101

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-006
(8883) Intro To Computer Science STAFF MW 3:30-4:45PM GCASL 279

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-007
(8884) Intro To Computer Science Gizem Kayar TR 3:30-4:45PM CIWW 101

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-008
(8887) Intro To Computer Science Hilbert Gene Locklear TR 12:30-1:45PM Kimmel 808

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-012
(8885) Intro To Computer Science STAFF MW 8:00-9:15AM GCASL 383

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0101-013
(8886) Intro To Computer Science STAFF TR 8:00-9:15AM Silv 208

Course Description:

How to design algorithms to solve problems and how to translate these algorithms into working computer programs. Experience is acquired through projects in a high-level programming language. Intended primarily for computer science majors but also suitable for students of other scientific disciplines. Programming assignments.

Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
CSCI-UA.0102-010
(8888) Data Structures Joanna Klukowska MW 9:30-10:45AM CIWW 109 Additional information: In-person recitation for Section 9

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-011
(8889) Data Structures - Recitation STAFF F 9:30-10:45AM CIWW 312 Additional information: In-person recitation for Section 10

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-012
(8890) Data Structures - Recitation STAFF F 11:00-12:15PM CIWW 312 Additional information: In-person recitation for Section 10

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-013
(8891) Data Structures - Recitation STAFF F 2:00-3:15PM CIWW 312 Additional information: In-person recitation for Section 10

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-020
(8892) Data Structures Evan Korth MW 12:30-1:45PM CIWW 109

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-021
(8893) Data Structures - Recitation STAFF F 9:30-10:45AM 12WV L120 Additional information: In-person recitation for Section 20

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-022
(8894) Data Structures - Recitation STAFF F 11:00-12:15PM Bobst LL138 Additional information: In-person recitation for Section 20

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-023
(8895) Data Structures - Recitation STAFF F 12:30-1:45PM Bobst LL138 Additional information: In-person recitation for Section 20

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-024 Data Structures - Recitation TBA F 2:00-3:15PM Silv 401 Additional information: In-person recitation for Section 20

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-030
(8896) Data Structures Anasse Bari F 9:30-12:00PM 5WP 101

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-031
(8897) Data Structures - Recitation STAFF M 9:30-10:45AM Tisch LC9 Additional information: In-person recitation for Section 30

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-033
(8899) Data Structures - Recitation STAFF W 9:30-10:45AM 12WV L120 Additional information: In-person recitation for Section 30

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-040
(8900) Data Structures Uday Kusupati TR 9:30-10:45AM 60 Fifth Ave 110 Additional information: In-Person Recitation for Section 3. Open to NYU Shanghai GoLocal students

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0102-041
(8901) Data Structures - Recitation STAFF F 9:30-10:45AM 60 Fifth Ave 110 Additional information: In-person recitation for Section 40

Course Description:

Use and design of data structures, which organize information in computer memory. Stacks, queues, linked lists, binary trees: how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them. Programming assignments.

Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
CSCI-UA.0201-010
(8902) Computer Systems Organization Douglas Moody MW 9:30-10:45AM CIWW 101 Additional information: Required Recitation is Section 11

Course Description:

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

Prerequisites: CSCI-UA.0102
CSCI-UA.0201-011
(8903) Computer Systems Organization - Recitation STAFF F 9:30-10:45AM 60 Fifth Ave 150 Additional information: Required Lecture is Section 10

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0201-020
(8904) Computer Systems Organization Hasan Aljabbouli TR 9:30-10:45AM Online

Course Description:

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

Prerequisites: CSCI-UA.0102
CSCI-UA.0201-021
(8905) Computer Systems Organization - Recitation STAFF F 9:30-10:45AM Online Additional information: Required Lecture is Section 20

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0201-030
(8906) Computer Systems Organization Gizem Kayar MW 3:30-4:45PM CIWW 109

Course Description:

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

Prerequisites: CSCI-UA.0102
CSCI-UA.0201-031
(8907) Computer Systems Organization - Recitation STAFF F 11:00-12:15PM 12WV G08

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102).
CSCI-UA.0202-001
(8908) Operating Systems Michael Walfish MW 11:00-12:15PM CIWW 109

Course Description:

Covers the principles and design of operating systems. Topics include process scheduling and synchronization, deadlocks, memory management (including virtual memory), input/output, and file systems. Programming assignments.

Prerequisites: Computer Systems Organization (CSCI-UA 201).
CSCI-UA.0202-002
(8909) Operating Systems Yang Tang TR 11:00-12:15PM Online

Course Description:

Covers the principles and design of operating systems. Topics include process scheduling and synchronization, deadlocks, memory management (including virtual memory), input/output, and file systems. Programming assignments.

Prerequisites: Computer Systems Organization (CSCI-UA 201).
CSCI-UA.0310-010
(8910) Basic Algorithms Chee Yap F 11:00-1:30PM Cant 101 Additional information: In-person recitation for Section 09

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-011
(8911) Basic Algorithms - Recitation STAFF M 9:30-10:45AM 194M 204

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-012
(8917) Basic Algorithms - Recitation STAFF T 9:30-10:45AM 194M 204

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-013
(8918) Basic Algorithms - Recitation STAFF W 9:30-10:45AM 194M 204

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-020
(8912) Basic Algorithms Alan Siegel TR 12:30-1:45PM GCASL 275

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-021
(8913) Basic Algorithms - Recitation Alan Siegel F 2:00-3:15PM Online Additional information: In-person recitation for Section 01

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-030
(8914) Basic Algorithms Amir Abboud MW 12:30-1:45PM GCASL 279

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-031
(8915) Basic Algorithms - Recitation Amir Abboud F 12:30-1:45PM Silv 208

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-040
(10763) Basic Algorithms Rachit Garg MW 3:30-4:45PM CIWW 317

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-041
(8916) Basic Algorithms - Recitation Rachit Garg F 8:00-9:15AM Tisch LC11 Additional information: Online Recitation for Section 3

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-050
(21736) Basic Algorithms Janani Sundaresan TR 3:30-4:45PM CIWW 317

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0310-051
(22012) Basic Algorithms - Recitation STAFF F 8:00-9:15AM CIWW 202

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
CSCI-UA.0330-001
MATH-UA.0144 Introduction to Computer Simulation Charles Peskin TR 11:00-12:15PM CIWW 201

Course Description:

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.0330-002 Introduction to Computer Simulation STAFF F 11:00-12:15PM CIWW 201 Additional information: In-person recitation for Section 01

Course Description:

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.0360-001
(19096) Drawing on the Web Joshua Clayton MW 3:30-4:45PM CIWW 312

Course Description:

The web is an intrinsically creative medium whose graphical capabilities are diverse and evolving. Across the HTML, CSS, SVG, and JavaScript specifications, there are multiple ways to code images and interaction natively in the browser. This course provides a survey of interrelated drawing techniques. We explore each of them through projects involving raster graphics, vector graphics, animation, and sound. Drawing on the Web builds on foundations in web design and computer programming with a focus on the expressive potential of the open web. Students contribute to our understanding of creative computation with their own research. Together we apply markup language, style language, and programming language to networked generative art.

Prerequisites:
Introduction to Web Design and Computer Principles (CSCI-UA 4) and Introduction to Computer Programming (CSCI-UA 2) or Introduction to Computer Science (CSCI-UA 101)
CSCI-UA.0381-001
(8940) Programming Tools for the Data Scientist Hasan Aljabbouli TR 3:30-4:45PM Kimmel 808

Course Description:

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
(8921) Numerical Computing Michael Overton TR 2:00-3:15PM CIWW 101

Course Description:

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.0430-001
(8948) Agile Software Development and DevOps Amos Bloomberg TR 12:30-1:45PM Online

Course Description:

Agile software development has come to describe a specific approach and toolset that allow for the requirements of a software project to change as a project progresses without disrupting schedules, budgets, and responsibilities. The field of DevOps, a portmanteau of development and operations has introduced further processes and infrastructure to automate many of the tasks required in such development. Together, Agile's methodology and DevOps' automation have increased the speed, robustness, and scalability with which software is developed today. Upon completion of this course, students will understand the core methodologies, technologies, and tools used in the software industry today.

Prerequisites: Computer Systems Organization (CSCI-UA 201)
CSCI-UA.0453-001
(8922) Theory Of Computation Marshall Ball MW 9:30-10:45AM 194M 203

Course Description:

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
(8941) Applied Internet Technology Joseph Versoza TR 9:30-10:45AM CIWW 101

Course Description:

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.0467-002
(8942) Applied Internet Technology Joseph Versoza MW 12:30-1:45PM CIWW 101

Course Description:

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
(8946) Natural Language Processing Adam Meyers TR 11:00-12:15PM Silv 520

Course Description:

Natural Language Processing (aka Computational Linguistics) is an inter-disciplinary field applying methodology of computer science and linguistics to the processing of natural languages (English, Chinese, Spanish, Japanese, etc.). Typical applications include: information extraction (automatically finding information from text); information retrieval (web searches and other applications involving the automatic selection of "relevant" documents); sentiment analysis (automatic extraction of opinions about a set of issues); and machine translation (automatically translating one natural language to another). Much of the best work in the field combines two methodologies: (1) automatically acquiring statistical information from one set of "training" documents to use as the basis for probabilistically predicting the distribution of similar information in new documents; and (2) using manually encoded linguistic knowledge. For example, many supervised methods of machine learning require: a corpus of text with manually encoded linguistic knowledge, a set of procedures for acquiring statistical patterns from this data and a transducer for predicting these same distinctions in new text. This class will cover linguistic, statistical and computational aspects of this exciting field.

Prerequisites: Computer Systems Organization (CSCI-UA 201).
CSCI-UA.0469-002
(8947) Natural Language Processing Adam Meyers TR 2:00-3:15PM Silv 208

Course Description:

Natural Language Processing (aka Computational Linguistics) is an inter-disciplinary field applying methodology of computer science and linguistics to the processing of natural languages (English, Chinese, Spanish, Japanese, etc.). Typical applications include: information extraction (automatically finding information from text); information retrieval (web searches and other applications involving the automatic selection of "relevant" documents); sentiment analysis (automatic extraction of opinions about a set of issues); and machine translation (automatically translating one natural language to another). Much of the best work in the field combines two methodologies: (1) automatically acquiring statistical information from one set of "training" documents to use as the basis for probabilistically predicting the distribution of similar information in new documents; and (2) using manually encoded linguistic knowledge. For example, many supervised methods of machine learning require: a corpus of text with manually encoded linguistic knowledge, a set of procedures for acquiring statistical patterns from this data and a transducer for predicting these same distinctions in new text. This class will cover linguistic, statistical and computational aspects of this exciting field.

Prerequisites: Computer Systems Organization (CSCI-UA 201).
CSCI-UA.0470-001
(8923) Object Oriented Programming Hasan Aljabbouli MW 9:30-10:45AM Online

Course Description:

Introduces the important concepts of object-oriented design and languages, including code reuse, data abstraction, inheritance, and dynamic overloading. Covers in depth those features of Java and C++ that support object-oriented programming and gives an overview of other object-oriented languages of interest. Significant programming assignments stressing object-oriented design. Not offered every academic year.

Prerequisites: Computer Systems Organization (CSCI-UA 201).
CSCI-UA.0472-001
(8924) Artificial Intelligence CANCELLED -

Course Description:
Many cognitive tasks that people can do easily and almost unconsciously have proven extremely difficult to program on a computer. Artificial intelligence tackles the problem of developing computer systems that can carry out these tasks. Focus is on three central areas in AI: representation and reasoning, machine learning, and natural language processing.

Prerequisites: Computer Systems Organization (CSCI-UA 201) and Basic Algorithms (CSCI-UA 310).
CSCI-UA.0473-010
(8935) Fundamentals of Machine Learning Eran Halperin MW 2:00-3:15PM CIWW 109

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).
CSCI-UA.0473-011
(8936) Fundamentals of Machine Learning - Recitation Fangshuo Liao F 9:30-10:45AM Kimmel 802

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).
CSCI-UA.0473-012
(21757) Fundamentals of Machine Learning - Recitation STAFF F 11:00-12:15PM Silv 405

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).
CSCI-UA.0473-013
(8937) Fundamentals of Machine Learning - Recitation Fangshuo Liao F 2:00-3:15PM Bobst LL138

Course Description:

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

Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).
CSCI-UA.0474-001 Software Engineering Amos Bloomberg TR 2:00-3:15PM Online

Course Description:

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.0475-001
(19272) Predictive Analytics Ahmad Emad TR 3:30-4:45PM 194M 206

Course Description:

Predictive analytics is the art and science of extracting useful information from historical data and present data for the purpose of predicting future trends. In this course, students will be introduced to the phases of the analytics life-cycle and will gain an understanding of a variety of tools and machine learning algorithms for analyzing data and discovering forward insights. Several techniques will be introduced including: data preprocessing techniques, data reduction algorithms, data clustering algorithms, data classification algorithms, uplifting algorithms, association rules, data mining algorithms, recommender systems, and more. This course aims to provide students with skills of the new generation of data scientists that will allow them to structure, analyze and derive useful insights from data that could help make better decisions.

Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA 310), and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148).
CSCI-UA.0476-001
(22531) Processing Big Data for Analytics Applications Ann Malavet TR 8:00-9:15AM CIWW 202

Course Description:

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 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.0479-001
(8943) Data Management and Analysis Joseph Versoza MW 11:00-12:15PM CIWW 101

Course Description:

Extracting, transforming and analyzing data in myriad formats. Using traditional 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 API 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.0479-002
(8944) Data Management and Analysis Matthew Zeidenberg TR 11:00-12:15PM Silv 206

Course Description:

Extracting, transforming and analyzing data in myriad formats. Using traditional 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 API 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.0479-003
(8945) Data Management and Analysis Matthew Zeidenberg MW 3:30-4:45PM CIWW 101

Course Description:

Extracting, transforming and analyzing data in myriad formats. Using traditional 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 API 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.0480-001
(22543) Special Topics: Computer Vision Davi Geiger TR 2:00-3:15PM GCASL 279

Course Description:

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

An introduction to the field of computer vision. Basic concepts will be covered such as edge detection, stereo vision, motion, color, texture and recognition.

Prerequisites: CSCI-UA.0201
CSCI-UA.0480-013
(19231) Special Topics: Designing Creative User Interfaces & Games Karl Rosenberg TR 2:00-3:15PM CIWW 317

Course Description:
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: CSCI-UA.0201
CSCI-UA.0480-041
(8926) Special Topics: Computer Graphics Gizem Kayar TR 12:30-1:45PM CIWW 101

Course Description:

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.0201
CSCI-UA.0480-051 Special Topics: Parallel Computing Mohamed Zahran TR 2:00-3:15PM Online

Course Description:

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: CSCI-UA.0201
CSCI-UA.0480-055
(8928) Special Topics: Principles of Programming Languages Joseph Tassarotti TR 2:00-3:15PM Tisch LC11

Course Description:

Computing professionals have to learn new programming languages all the time. This course teaches the fundamental principles of programming languages that enable students to learn new languages quickly and help them decide which one is best suited for a given task. Students will learn new ways of viewing computation and programs, and new ways of approaching algorithmic problems, making them better programmers overall. Topics include recursion and induction, algebraic data types and pattern matching, higher-order functions, continuations and tail recursion, programming language syntax and semantics, type systems, and objects and classes. The course will explore this material by building interpreters for programming languages of increasing complexity. The course will thus be accompanied by extensive programming assignments.

Prerequisites: CSCI-UA.0201
CSCI-UA.0480-061
(8929) Special Topics: Open Source Software Development Joanna Klukowska MW 12:30-1:45PM 60 Fifth Ave 110

Course Description:

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.

Prerequisites: CSCI-UA.0201
CSCI-UA.0480-062
(8930) Special Topics: Computer Networks Jinkun Geng MW 9:30-10:45AM GCASL 369

Course Description:

This course will introduce students to computer networks, using today's Internet as an example. Topics covered include socket programming, routing, forwarding, reliable transmission, congestion control, and medium access control. The course will involve a good deal of programming in Python. Familiarity with programming, algorithms, and basic probability is expected. Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA.0310), and Discrete Mathematics (Math UA 120); or permission of the department.

Prerequisites: CSCI-UA.0201
CSCI-UA.0480-074
(8931) Special Topics: Quantum Computing Fermi Ma TR 11:00-12:15PM GCASL 275

Course Description:

Quantum computing is an interdisciplinary field that lies at the
intersection of computer science, mathematics, and physics. This
paradigm relies on principles of quantum mechanics, such as
superposition and entanglement, to perform complex computation. This
course aims to provide a self-contained, comprehensive introduction to
quantum computing, focusing on the design and analysis of quantum
algorithms, as well as covering topics in quantum information and
quantum cryptography, such as quantum teleportation, quantum money, and
nonlocal games.

Prerequisites: CSCI-UA.0201
CSCI-UA.0521-001 Undergraduate Research STAFF -

Course Description:
The student is supervised by a faculty member actively engaged in research, possibly leading to results publishable in the computer science literature. A substantial commitment to this work is expected. The research project may be one or two semesters, to be determined in consultation with the faculty supervisor. Students taking this course for honors in computer science are required to write an honors thesis. All other students need to submit a write-up of the research results at the conclusion of the project.

Prerequisites: Permission of the department.
CSCI-UA.0898-001 Internship STAFF -

Course Description:
An internship in computer science is an excellent complement to formal course work. We strongly recommend that students have some practical training along with their classroom experience, so they can explore different career options and gain hands-on experience. An internship is for majors only, and students must have maintained an overall GPA of 3.0 and a computer science GPA of 3.5. The internship will be graded.

Prerequisites: Restricted to declared computer science majors. Internship credit does not count toward major requirements, but does apply toward completion of the CAS degree.
CSCI-UA.0998-001 Independent Study STAFF -

Course Description:

Students majoring in the department are permitted to work on an individual basis under the supervision of a full-time faculty member in the department if they have maintained an overall GPA of 3.0 and a GPA of 3.5 in computer science and have a study proposal that is approved by the director of undergraduate studies. Students are expected to spend about three to six hours a week on their project.

Prerequisites: Permission of the department. Does not satisfy the major elective requirement.

*

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

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