Course Schedule

Current semester Fall 2026, and future semesters are now available to view.

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• CSCI-GA.1170-​001
  DS-GA.1170-001
  (5644) Fundamental Algorithms  Chee Yap M 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.

  
  
  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
  (5645) Fundamental Algorithms Recitation  TBA F 11:15-12:05PM Silv 405 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.

  
  
  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
  BMIN-GA.1170-001
  (5646) Fundamental Algorithms  Julia Kempe T 10:15-12:15PM Online 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.

  
  
  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-​006
  DS-GA.1170-002
  (5647) Fundamental Algorithms Recitation  TBA F 2:45-3:35PM 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.

  
  
  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-​007
  (5648) Fundamental Algorithms Recitation  TBA R 11:15-12:05PM 60 Fifth Ave 110 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.

  
  
  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
  BMIN-GA.1180-001
  (5649) Mathematical Techniques For CS Applications  Parijat Dube W 7:45-9:45PM Online
  

  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
  (5650) Programming Languages  Joseph Tassarotti T 4:55-6:55PM Meyr 121
  

  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
  (5651) Programming Languages Recitation  TBA F 12:30-1:20PM Meyr 122 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
  (5652) Programming Languages  Cory Plock M 4:55-6:55PM Silv 401 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-​005
  (5653) Programming Languages Recitation  TBA F 11:15-12:05PM Silv 401 Additional information: Recitation for Section 003
  

  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-​006
  (5654) Programming Languages Recitation  TBA F 4:55-5:45PM 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.2112-​001
  MATH-GA 2043-001
  (5722) Scientific Computing   Benjamin Peherstorfer TR 4:55-6:10PM 194M 203
  

  Course Description:

  Methods for numerical applications in the physical and biological sciences, engineering, and finance. Basic principles and algorithms; specific problems from various application areas; use of standard software packages.
  
  Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
• CSCI-GA.2250-​001
  (5655) Operating Systems  Hubertus Franke M 7:10-9:10PM 60 Fifth Ave 150
  

  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
  (5656) Operating Systems  Yang Tang R 4:55-6:55PM Silv 414
  

  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
  (5657) Data Communications and Networks  Jean-Claude Franchitti R 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.2270-​001
  (5658) Computer Graphics   Kenneth Perlin W 7:10-9:10PM 194M 304
  

  Course Description:

  Problems and objectives of computer graphics. Vector, curve, and character generation. Interactive display devices. Construction of hierarchical image list. Graphic data structures and graphics languages. Hidden-line problems; windowing, shading, and perspective projection. Curved surface generation display.

  
  
  Prerequisites: CSCI-GA 1170 and CSCI-UA 140 (or an equivalent undergraduate course in linear algebra).
• CSCI-GA.2271-​001
  (5659) Computer Vision   Saining Xie R 7:10-9:10PM 12WP G08
  

  Course Description:

  Basic techniques of computer vision and image processing. General algorithms for image understanding problems. Study of binary image processing, edge detection, feature extraction, motion estimation, color processing, stereo vision, and elementary object recognition. Mathematical, signal processing, and image processing tools. Relation of computer vision algorithms to the human visual system.

  
  
  Prerequisites: CSCI-GA 1170
• CSCI-GA.2390-​001
  (21646) Logic In Computer Science  Ernest Davis R 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.2420-​001
  MATH-GA 2010-001 Numerical Methods I   TBA MW 4:55-6:10PM Silv 208
  

  Course Description:

  Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.
  
  Prerequisites: Corequisite: linear algebra.
• CSCI-GA.2433-​001
  DS-GA.2433-001
  (5660) Database Systems  Jean-Claude Franchitti R 4:55-6:55PM 60 Fifth Ave 150
  

  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.2434-​001
  (5661) Advanced Database Systems  Dennis Shasha R 4:55-6:55PM CIWW 101
  

  Course Description:

  This is a capstone course emphasizing large-scale database systems. This course studies the internals of database systems as an introduction to research and as a basis for rational performance tuning. Topics include concurrency control, fault tolerance, operating system interactions, query processing, and principles of tuning. Database capstone projects involve topics such as design, concurrency control, interactions, and tuning. These projects include some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
• CSCI-GA.2436-​001
  (5719) Realtime and Big Data Analytics  Yang Tang R 7:10-9:10PM Silv 414
  

  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
  (5720) Big Data Application Development  Yang Tang T 7:10-9:10PM Silv 208
  

  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.2560-​001
  (5662) Artificial Intelligence   Paul Bethe M 4:55-6:55PM 194M 206
  

  Course Description:

  There are many cognitive tasks that people do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course covers problem solving and state space search; automated reasoning; probabilistic reasoning; planning; and knowledge representation.

• CSCI-GA.2565-​001
  (5663) Machine Learning   Kyunghyun Cho T 4:55-6:55PM CANTR 101
  

  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
  (5382) Deep Learning  Yann LeCun
  Alfredo Canziani M 12:30-2:30PM Cant 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
  (5384) Deep Learning Lab  Alfredo Canziani M 2:45-3:35PM Cant 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
  BMIN-GA 2590-001
  (22249) Natural Language Processing  STAFF W 4:55-6:55PM Silv 401
  

  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.2630-​003
  (5708) Foundations of Networks and Mobile Systems*  Lakshminarayanan Subramanian T 10:15-12:15PM Silv 208 Additional information: Update for Fall 2023, MSCS students can request a permission code by emailing graduate@cs.nyu.edu
  

  Course Description:

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

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.

• CSCI-GA.2630-​004
  (5709) Foundations of Networks and Mobile Systems Lab*  Lakshminarayanan Subramanian T 12:30-1:20PM Silv 208 Additional information: Lab for Section 003
  

  Course Description:

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

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.

• CSCI-GA.2810-​001
  (5710) Design and Innovation*  Gary Zamchick F 10:15-12:15PM Silv 407 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 is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

  
  
  Prerequisites: None
• CSCI-GA.2810-​002
  (5711) Design and Innovation Lab*  Gary Zamchick F 12:30-1:20PM Silv 407 Additional information: Lab for Section 001
  

  Course Description:

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

  This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

  
  
  Prerequisites: None
• CSCI-GA.2820-​001
  (5712) DevOps and Agile Methodologies  John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program
  

  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
  (5713) DevOps and Agile Methodologies Lab  John Rofrano T 7:10-8:00PM Online Additional information: Lab for Section 001. Open only to students in the Stern TECH MBA program.
  

  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
  (5714) 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
  (5715) 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.2840-​001
  (5716) Entrepreneurship Capstone  Evan Korth M 10:15-12:15PM 194M 306A
  

  Course Description:

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

  The Entrepreneurship Capstone class is a 15­week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.

• CSCI-GA.2840-​002
  (5717) Entrepreneurship Capstone Lab  TBA M 12:30-1:20PM 60 Fifth Ave 110 Additional information: Lab for Section 001
  

  Course Description:

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

  The Entrepreneurship Capstone class is a 15­week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.

• CSCI-GA.2965-​001
  (5664) Heuristic Problem Solving  Dennis Shasha R 7:10-9:10PM CIWW 312
  

  Course Description:

  This course revolves around several problems new to computer science (derived from games or puzzles in columns for Dr. Dobb’s Journal, Scientific American, and elsewhere). The idea is to train students to face a new problem, read relevant literature, and come up with a solution. The solution entails winning a contest against other solutions. The winner receives candy. The best solutions become part of an evolving “Omniheurist” Web site that is expected to get many visitors over the years. The course is for highly motivated, mathematically adept students. It is open to supported Ph.D. students and well-qualified master’s students. Class size has been around 10 in the past, and instructor and students have all gotten to know one another very well. Algorithmic and programming knowledge is the main prerequisite. It also helps to be familiar with a rapid prototyping language such as Matlab, Mathematica, K, or Python, or to be completely fluent in some other language.
  
  Prerequisites: CSCI-GA 1170 and an ability to prototype algorithms rapidly.
• CSCI-GA.3033-​025
  (5684) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming  Mohamed Zahran W 4:55-6:55PM Online
  

  Course Description:

  This course serves as a capstone for the MSCS program.
  
  In this course, we will cover architectural aspects and capabilities of modern GPUs (graphics processing unit) and will learn how to program GPUs to solve different type of problems. Many computations can be performed much faster on the GPU than on a traditional processor. This is why GPUs are present now in almost all computers; and the majority of Top 500 supercomputers in the world are built around GPUs. GPUs are no longer restricted to graphics applications but are now used for a diverse set of applications and domains. This introduces the concept of general-purpose GPUs or GPGPUs, which is the main subject of this course.

  
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Basic understudying of operating systems and computer architecture, ability to program in at least one of c/c++, go, or Python languages. Basic understanding of cloud computing systems.
• CSCI-GA.3033-​061
  (5685) Special Topics: Predictive Analytics  Anasse Bari R 4:55-6:55PM GCASL 261
  

  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 have an introduction to the phases of the analytics lifecycle and a basic understanding of a variety of tools and machine learning techniques to analyze data to discover forward insights. Several techniques will be introduced; including data pre-processing techniques, data clustering algorithms, data classification algorithms, association rules data mining algorithms, recommender systems, etc.
  
  Applications from bio-informatics, social networks analytics, and text mining will be covered. Highlights from industrial use cases will be covered to demonstrate how Predictive Analytics relates to improving business performance and impacting better decisions. This is an introductory course that will provide students with basic 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: Prerequisites vary according to topic.
• CSCI-GA.3033-​078
  (5687) Special Topics: Cryptocurrencies and Decentralized Ledgers  Joseph Bonneau M 7:10-9:10PM 60 Fifth Ave 110
  

  Course Description:

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java. An undergraduate course in cryptography or successful completion of CSCI-GA.3205 or CSCI-GA.3210 strongly recommended.
• CSCI-GA.3033-​085
  (5688) Special Topics: Cloud and Machine Learning  I-Hsin Chung
  Eun Kyung Lee R 4:55-6:55PM 60 Fifth Ave 110
  

  Course Description:

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

  
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Python programming. Students are expected to know basic Linux commands and configurations. Software communication and development tools such as Slack and Github will be used.
• CSCI-GA.3033-​091
  (5690) Special Topics: Introduction to Deep Learning and LLM based Generative AI Systems  Parijat Dube
  Chen Wang F 7:10-9:10PM Tisch LC11 Additional information: Lab for CSCI-GA.3033-​083
  

  Course Description:

  The course will cover several topics related to Deep Learning (DL) systems and their performance. Both algorithmic and system related building blocks of DL systems will be covered including DL training algorithms, network architectures, and best practices for performance optimization. We will study hyperparameter choices, scalable distributed DL training, Kubernetes based DL system stack on cloud, tools and benchmarks for performance evaluation of DL systems, transfer learning etc. Emphasis will be on getting working knowledge of tools and techniques for performance evaluation of DL systems. The students will gain practical experience working on different stages of DL life cycle, including model development, testing, and deployment. The assignments will be mostly hands-on involving standard DL frameworks (Tensorflow, Pytorch) and open source technologies.

  
  
  Prerequisites: An introductory machine learning course. Knowledge of python is preferred but not necessary.
• CSCI-GA.3033-​110
  (5691) Special Topics: Technologies for Finance  Anirudh Sivaraman M 7:10-9:10PM CIWW 101
  

  Course Description:

  This is a new masters seminar that will explore topics at the intersection of finance and computing technologies. The course will include a mix of lectures from the instructor, lectures from invited speakers, and assigned readings.
  
  Tentative list of topics:
  Network technologies: microwave links, FPGAs, layer 1 switches, multicast, programmable switches, Cloud technologies: Challenges of operating exchange infrastructure in the cloud, Synchronized clocks: The role of synchronized time in stock exchanges: PTP, WhiteRabbit, PPS, Software solutions, etc.; High-frequency trading: History, trading algorithms, limit order books, market making, speed races, batch auctions, AI meets finance: BloombergGPT and related technologies, Secure payments, Programming language techniques: OCaml, Confidential computing for fintech, Central bank digital currencies, Distributed systems: Consensus, designing central exchange servers, etc., Dark pools

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​121
  (5692) Special Topics: Programming Parallel Algorithms  Sam Westrick M 10:15-12:15PM 60 Fifth Ave C10
  

  Course Description:

  This course considers the challenge of converting high-level algorithmic ideas into efficient parallel code. We will see how this challenge is greatly simplified by modern programming languages and programming techniques, especially functional programming techniques which make it possible to quickly develop efficient, scalable, and correct implementations. Prior experience with functional programming is helpful but not required. Students will learn how to design, analyze, implement, and evaluate the performance of parallel algorithms across a variety of problem domains (e.g., graph analysis, computational geometry, numerical algorithms, image processing, etc.). In the second half of the course, students will complete a parallel programming project of their own design.

  
  
  Prerequisites: CSCI-GA.2110-001 Programming Languages. Students should be comfortable with recursive programming techniques and familiar with basic data structures and algorithms.
• CSCI-GA.3033-​125
  (5692) Special Topics: Reinforcement Learning with Foundation Models  Sherry Yang R 4:55-6:55PM Online
  

  Course Description:

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​127
  ECE-GY.9483-A
  (5694) Special Topics: Efficient AI Computing: Algorithm and Implementation  Sai Qian Zhang W 7:10-9:10PM Tisch LC11
  

  Course Description:

  The course will focus on recent advancements in the design of efficient neural networks, specifically on how to create and optimize AI models for improved performance, and resource efficiency. Students will explore essential techniques such as pruning, quantization, and model distillation across different model architectures like CNNs, RNNs, Transformers, and LLMs. These techniques aim to reduce computational complexity while preserving accuracy. Additionally, the course will cover efficient training and inference methods, including distributed computing, parallelism, and low-precision computation, essential for deploying AI on resource-constrained platforms. Lastly, students will gain a foundational understanding of computer architectures and learn how to deploy AI algorithms on actual edge devices.

  
  
  Prerequisites: CSCI-GA.2565-001 Machine Learning or equivalent
• CSCI-GA.3033-​134
  (5696) Special Topics: The Science of Deep Learning  Pratyusha Sharma M 2:45-4:45PM 60 Fifth Ave C10
  

  Course Description:

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​135
  (5697) Special Topics: Computational Genomics  Eran Halperin T 2:45-4:45PM 60 Fifth Ave 125
  

  Course Description:

  This course will bridge classical statistical genomics and modern deep learning, taking students from maximum likelihood estimation through large-scale genomic foundational models. By the end of the semester, students will have hands-on experience with state-of-the-art methods applied to real genomic data.

  
  
  Prerequisites: Python programming experience. Previous coursework in probability, statistics, and linear Algebra. Familiarity with machine learning fundamentals is helpful but not required.
• CSCI-GA.3033-​137
  (5698) Special Topics: Information Theory and Applications in CS  Marshall Ball M 12:30-2:30PM CIWW 317
  

  Course Description:

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​138
  (5699) Special Topics: Approximation Algorithms  Anupam Gupta R 2:45-4:45PM CIWW 202
  

  Course Description:

  The area of approximation algorithms is aimed at giving provable guarantees on the performance of heuristics for hard problems. The course will cover techniques developed to obtain such algorithms, including the use of linear and convex programming, the use of randomness and linear-algebraic methods, and metric embeddings. Along algorithmic approaches, we will discuss techniques to prove inapproximability, i.e., conditional limits to how well problems can be approximated (typically assuming conjectures like P neq NP, or the UGC). Finally, we will also discuss some ideas behind online algorithms, which studies decision-making under uncertainty, and shared several techniques with approximation algorithms. The aim of the course is to expose students to ideas from all of these threads of research.

  Prerequisites: 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 course. Familiarity with probability theory and linear algebra. Mathematical maturity. If you are not sure you meet the prerequisites, feel free to email the instructor.

   

   

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​139
  (5700) Special Topics: Quantum Cryptography  Fermi Ma M 2:45-4:45PM Bobst LL150
  

  Course Description:

  Quantum computers will profoundly change the landscape of modern cryptography. Most famously, Shor’s algorithm will enable quantum computers to break much of the cryptography that currently secures the internet. But the impact of quantum computers on cryptography goes far beyond breaking existing cryptosystems. Quantum computers will also open the door to new kinds of cryptographic protocols that leverage quantum phenomena. In recent years, there has been an explosion of activity in this area. Not only has there been a flurry of new quantum cryptographic protocols, but surprisingly, studying these protocols has yielded deep insights into computational complexity and even fundamental physics.
  
  The goal of this course will be to survey recent developments at the intersection of quantum computing and cryptography, with an emphasis on basic concepts and techniques. Since this is a rapidly evolving area, this course will also highlight key open questions and encourage students to formulate their own research problems.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​140
  (5701) Special Topics: Honors Machine Learning  Gautam Kamath T 4:55-6:55PM Silv 401
  

  Course Description:

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​141
  (5702) Special Topics: Theoretical Foundations in Modern Machine Learning  Allen Liu T 4:55-6:55PM Bobst LL138
  

  Course Description:

  The overall goal of this class is to explore various ways that theoretical insights play a role in advancing modern machine learning. We will examine a variety of domains such as diffusion models, large language models, and reinforcement learning, with an emphasis on how theory leads to new practical discoveries.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​142
  (5703) Special Topics: Quantum Information  Tony Metger W 4:55-6:55PM CIWW 317
  

  Course Description:

   

  This course introduces quantum information theory, the study of how quantum systems act as information carriers. This forms the mathematical foundation for many exciting applications in quantum computing and cryptography. We will explore whether we can use quantum systems and entanglement to transmit classical information more efficiently, how to quantify the amount of quantum information in a system, how to extract classical information from quantum systems, and the role symmetry plays in these tasks. The focus will be on understanding these questions rigorously and introducing mathematical tools that are broadly useful in quantum computing research. A strong background in linear algebra and mathematical maturity are required, but no prior knowledge of quantum physics, theoretical computer science, or representation theory is necessary. This course is suitable for theory-minded graduate students in CS, physics, and math.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​148
  (21778) Special Topics: Learning to Match Distributions: Optimal Transport, Flow Matching & Applications  Romain Lopez M 4:55-6:55PM
  

  Course Description:

  This special topics course explores how to match and transform probability distributions for machine learning applications. The course begins with a few lectures on optimal transport and flow matching, and transitions into a reading group covering contemporary methods and applications across scientific domains. A background in probability and statistics is expected; familiarity with convex optimization is helpful but not required.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​925
  (5706) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Recitation  Mohamed Zahran F 11:15-12:05PM Meyr 122 Additional information: Recitation for Section 925
  

  Course Description:

  This is the recitation for section 925. Refer to the lecture for description.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3110-​001
  (5666) Honors Programming Languages  Patrick Cousot TR 3:30-4:45PM 60 Fifth Ave 261
  

  Course Description:

  The course will introduce a panorama of programming languages concepts underlying the main programming language paradigms (such as imperative, functional, object-oriented, logic, concurrent, and scripting languages) and present in detail the formal methods (code semantics, specification, and verification) used in modern high quality assurance tools for software safety and security. A programming project (design and implementation of an interpreter/compiler for an dynamic object-oriented mini-language) will be programmed in OCaml, a multiparadigm language introduced at the beginning of the course.

  
  
  Prerequisites: CSCI-GA.2110 Programming Languages, CSCI-GA.3110 Honors Programming Languages
• CSCI-GA.3205-​001
  (5667) Applied Cryptography & Network Security  Mazdak Zamani W 4:55-6:55PM 60 Fifth Ave 110
  

  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.3210-​001
  MATH-GA 2170-001
  (5668) Introduction To Cryptography  Nir Bitansky W 10:15-12:15PM 60 Fifth Ave C10
  

  Course Description:

  The primary focus of this course is on definitions and constructions of various cryptographic objects, such as pseudorandom generators, encryption schemes, digital signature schemes, message authentication codes, block ciphers, and others, time permitting. The class tries to understand what security properties are desirable in such objects, how to properly define these properties, and how to design objects that satisfy them. Once a good definition is established for a particular object, the emphasis will be on constructing examples that provably satisfy the definition. Thus, a main prerequisite of this course is mathematical maturity and a certain comfort level with proofs. Secondary topics, covered only briefly, are current cryptographic practice and the history of cryptography and cryptanalysis.
  
  Prerequisites: Strong mathematical background
• CSCI-GA.3520-​001
  (5665) Honors Analysis of Algorithms  Subhash Khot TR 9:30-10:45AM Silv 401
  

  Course Description:

  Design of algorithms and data structures. Review of searching, sorting, and fundamental graph algorithms. In-depth analysis of algorithmic complexity, including advanced topics on recurrence equations and NP-complete problems. Advanced topics on lower bounds, randomized algorithms, amortized algorithms, and data structure design as applied to union-find, pattern matching, polynomial arithmetic, network flow, and matching.
  
  Prerequisites: Permission of the instructor for master’s students.
• CSCI-GA.3812-​001
  (5707) Information Technology Projects *  Jean-Claude Franchitti M 6:10-9:10PM Silv 509
  

  Course Description:

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

  
  
  Prerequisites: For MS in IS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms and two of the following three courses: CSCI-GA 2262 Data Communications & Networks; CSCI-GA 2250 Operating Systems; CSCI-GA 2433 Database Systems. For MS in CS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms, CSCI-GA 2110 Programming Languages, and CSCI-GA 2250 Operating Systems. PERMISSION OF THE DEPARTMENT REQUIRED.
• CSCI-GA.3813-​001
  (5669) 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
  (5670) 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
  (5671) 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
  (5672) 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
  (5673) 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
  (5674) 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
  (5675) 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
  (5676) 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
  (5677) 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
  (5678) PhD Seminar: Cryptography **  Yevgeniy Dodis T 11:00-1:50PM CIWW 317 Additional information: This course also meets at CIWW 317. Please check with your instructor to confirm the schedule.
  

  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
  (5679) PhD Seminar: Theory  Anupam Gupta R 11:00-1:50PM CIWW 317
  

  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
  (5680) PhD Seminar: Formal Methods  Thomas Wies
  Joseph Tassarotti 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
  (5681) 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
  (5682) 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
  (5683) 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
  (21698) Computers & Society: Understanding AI: Technology Transforming Society  Alfredo Canziani TR 3:30-4:45PM Meyr 121
  

  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
  (21896) Computers and Society: Introduction to Computer Programming with AI Assistance  Craig Kapp MW 2:00-3:15PM Kimmel 808
  

  Course Description:

  An introduction to the fundamentals of computer programming and computational thinking. Students design, write, and debug programs while learning to collaborate effectively with artificial intelligence tools. The course emphasizes problem decomposition, system design, and the evaluation of code. No prior knowledge of programming or AI is assumed.
  
  Prerequisites: No prior computing experience is assumed. Not intended for computer science majors.
• CSCI-UA.0001-​003
  (21907) Computers and Society: Introduction to Computer Programming with AI Assistance  Matthew Zeidenberg TR 8:00-9:15AM 194M 206
  

  Course Description:

  An introduction to the fundamentals of computer programming and computational thinking. Students design, write, and debug programs while learning to collaborate effectively with artificial intelligence tools. The course emphasizes problem decomposition, system design, and the evaluation of code. No prior knowledge of programming or AI is assumed.
  
  Prerequisites: No prior computing experience is assumed. Not intended for computer science majors.
• CSCI-UA.0002-​001
  (8896) Intro To Computer Programming (No Prior Experience)  Craig Kapp MW 8:00-9:15AM Bobst LL138
  

  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
  (8897) Intro To Computer Programming (No Prior Experience)  TBA TR 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-​004
  (8899) Intro To Computer Programming (No Prior Experience)  TBA TR 9:30-10:45AM Bobst LL138
  

  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
  (8900) Intro To Computer Programming (No Prior Experience)  Jiri Motejlek MW 11:00-12:15PM 60 Fifth Ave 110
  

  Course Description:

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​006
  (8901) Intro To Computer Programming (No Prior Experience)  TBA MW 12:30-1:45PM 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-​007
  (8902) 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-​008
  (8903) Intro To Computer Programming (No Prior Experience)  Erik Larsen TR 2:00-3:15PM 60 Fifth Ave 110
  

  Course Description:

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​009
  (8904) Intro To Computer Programming (No Prior Experience)  Joshua Clayton MW 3:30-4:45PM 194M 206
  

  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)  TBA TR 3:30-4:45PM GCASL 388
  

  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-​013
  (22007) Intro To Computer Programming (No Prior Experience)  Amanda Steigman MW 8:00-9:15AM Online 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.0004-​001
  (8908) Intro to Web Design & Computer Principles  Alvaro Olsen MW 8:00-9:15AM 60 Fifth Ave 150
  

  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
  (8909) 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
  (8910) Intro to Web Design & Computer Principles  Mark Ramos MW 11:00-12:15PM 60 Fifth Ave 150
  

  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
  (8911) Intro to Web Design & Computer Principles  Joshua Clayton TR 11:00-12:15PM 194M 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
  (8912) Intro to Web Design & Computer Principles  Mark Ramos MW 2:00-3:15PM 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-​006
  (8913) Intro to Web Design & Computer Principles  Alvaro Olsen MW 3:30-4:45PM Silv 411
  

  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
  (8915) Database Design And Implementation  Mark Ramos TR 11:00-12:15PM GCASL 361
  

  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
  (8916) Web Development And Programming  Sana Odeh MW 2:00-3:15PM 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
  (8917) Intro To Computer Science  Nader Nassar MW 8:00-9:15AM 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-​002
  (8918) Intro To Computer Science  STAFF TR 8:00-9:15AM 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-​003
  (8919) Intro To Computer Science  Sana Odeh MW 11:00-12:15PM 194M 206
  

  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
  (8914) Intro To Computer Science  Gizem Kayar TR 11:00-12:15PM 194M 203
  

  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
  (8920) Intro To Computer Science  Hilbert Gene Locklear MW 12:30-1:45PM 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-​006
  (8921) Intro To Computer Science  Candido Cabo TR 12:30-1:45PM Silv 401
  

  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
  (8922) Intro To Computer Science  Anasse Bari F 2:00-4:45PM 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-​008
  (8923) Intro To Computer Science  Sana Odeh 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-​009
  (8924) Intro To Computer Science  Tobias Blickhan MW 9:30-10:45AM GCASL 369
  

  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
  (8927) Data Structures  TBA MW 9:30-10:45AM Silv 408 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
  (8928) Data Structures - Recitation  TBA F 9:30-10:45AM CIWW 317 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
  (8929) Data Structures - Recitation  TBA F 11:00-12:15PM CIWW 317 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
  (8930) Data Structures - Recitation  TBA F 12:30-1:45PM CIWW 317 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
  (8932) 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
  (8933) Data Structures - Recitation  TBA F 11:00-12:15PM CIWW 101 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
  (8934) Data Structures - Recitation  TBA F 12:30-1:45PM CIWW 101 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
  (8935) Data Structures - Recitation  TBA F 2:00-3:15PM CIWW 101 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
  (8939) Data Structures  Anasse Bari F 11:00-1:30PM Silv 408
  

  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
  (8941) Data Structures - Recitation  TBA M 9:30-10:45AM 194M 206 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-​032
  (8942) Data Structures - Recitation  TBA W 9:30-10:45AM 194M 206 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
  (8943) Data Structures  TBA TR 9:30-10:45AM CIWW 312 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
  (8944) Data Structures - Recitation  TBA F 9:30-10:45AM CIWW 202 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.0102-​050
  (8945) Data Structures  Federico Sichetti MW 2:00-3:15PM CIWW 312
  

  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-​051
  (8946) Data Structures - Recitation  TBA F 2:00-3:15PM CIWW 317 Additional information: In-person recitation for Section 50
  

  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-​060
  (21314) Data Structures  Romain Cosson TR 3:30-4:45PM GCASL 261
  

  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-​061
  (21347) Data Structures - Recitation  TBA F 3:30-4:45PM Additional information: In-person recitation for Section 60
  

  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
  (8947) Computer Systems Organization  Douglas Moody MW 9:30-10:45AM CIWW 101 Additional information: Recitation for Section 7
  

  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
  (8948) Computer Systems Organization - Recitation  TBA F 9:30-10:45AM GCASL 279 Additional information: Recitation for Section 1
  

  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
  (8949) Computer Systems Organization  Hasan Aljabbouli TR 9:30-10:45AM Online 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
  (8952) Computer Systems Organization - Recitation  TBA F 2:00-3:15PM Online Additional information: Online Recitation for Section 1
  

  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
  (8951) Computer Systems Organization  Jinyang Li MW 3:30-4:45PM 60 Fifth Ave 150
  

  Course Description:

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

  
  
  Prerequisites: CSCI-UA.0102
• CSCI-UA.0201-​031
  (8952) Computer Systems Organization - Recitation  TBA F 12:30-1:45PM Silv 206
  

  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-​040
  (8953) Computer Systems Organization  Gizem Kayar TR 3:30-4:45PM 60 Fifth Ave 150 Additional information: In-Person Recitation for Section 3. Open to NYU Shanghai GoLocal students
  

  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-​041
  (8954) Computer Systems Organization - Recitation  TBA F 11:00-12:15PM Silv 206 Additional information: Online Recitation for Section 3
  

  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
  (8955) 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
  (8956) Operating Systems  Mohamed Zahran TR 3:30-4:45PM Online 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
  (8957) Basic Algorithms  Ernest Davis MW 9:30-10:45AM CIWW 109 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
  (8958) Basic Algorithms - Recitation  TBA F 11:00-12:15PM 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-​012
  (22021) Basic Algorithms - Recitation  TBA F 9:30-10:45AM Silv 401
  

  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
  (8959) Basic Algorithms  TBA MW 2:00-3:15PM 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.0310-​021
  (8960) Basic Algorithms - Recitation  TBA F 2:00-3:15PM CIWW 202 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
  (8961) Basic Algorithms  TBA TR 9:30-10:45AM Online Online
  

  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
  (8962) Basic Algorithms - Recitation  TBA F 9:30-10:45AM Online
  

  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
  (8963) Basic Algorithms  TBA TR 3:30-4:45PM 60 Fifth Ave 110
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​041
  (8964) Basic Algorithms - Recitation  TBA F 9:30-10:45AM 194M 307 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.0380-​001
  (5702) Tpcs General Interest: Fund Of Comp Sci In C  STAFF T 4:55-6:55PM Silv 414
  

  Course Description:

  In this course you will learn how to develop multimedia applications for both the desktop and the web. The course focuses on general programming techniques with an emphasis on the production of interactive graphical environments. In addition, a number of advanced topics will be covered including image processing, video games, simulations, basic computer vision, augmented reality (AR), virtual reality (VR) and data visualization techniques.

  
  
  Prerequisites: CSCI-UA.0002, CSCI-UA.0004
• CSCI-UA.0381-​001
  (8965) Programming Tools for the Data Scientist  Hasan Aljabbouli MW 2:00-3:15PM Silv 206
  

  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.0430-​001
  (8994) Agile Software Development and DevOps  TBA MW 9:30-10:45AM 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.0467-​001
  (8986) Applied Internet Technology  TBA TR 12:30-1:45PM Silv 520
  

  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
  (8987) Applied Internet Technology  TBA MW 12:30-1:45PM Silv 411
  

  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
  (8992) Natural Language Processing  Adam Meyers TR 8:00-9:15AM 60 Fifth Ave 150
  

  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
  (8993) Natural Language Processing  Adam Meyers TR 3:30-4:45PM Silv 401
  

  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
  (8969) Object Oriented Programming  Hasan Aljabbouli MW 3:30-4:45PM Silv 206
  

  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.0473-​001
  (8980) Fundamentals of Machine Learning  TBA TR 12:30-1:45PM Cant 101
  

  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-​002
  (8981) Fundamentals of Machine Learning - Recitation  TBA F 9:30-10:45AM 60 Fifth Ave 150 Additional information: In-person recitation for Section 01
  

  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-​021
  (8982) Fundamentals of Machine Learning - Recitation  TBA F 2:00-3:15PM 60 Fifth Ave 150 Additional information: In-person recitation for Section 01
  

  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
  (8970) Software Engineering  TBA MW 12:30-1:45PM 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.0478-​001
  MATH-UA.0243
  (8972) Introduction To Cryptography  TBA TR 2:00-3:15PM GCASL 261
  

  Course Description:

  An introduction to the principles and practice of cryptography and its application to network security. Topics include symmetric-key encryption (block ciphers, modes of operations, AES), message authentication (pseudorandom functions, CBC-MAC), public-key encryption (RSA, ElGamal), digital signatures (RSA, Fiat-Shamir), and authentication applications (identification, zero-knowledge).

  
  
  Prerequisites:

  Basic Algorithms (CSCI-UA 310).

• CSCI-UA.0479-​001
  (8988) Data Management and Analysis  TBA 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-​003
  (8990) Data Management and Analysis  Matthew Zeidenberg MW 3:30-4:45PM Kimmel 808
  

  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-​004
  (8991) Data Management and Analysis  Matthew Zeidenberg TR 2:00-3:15PM Silv 401
  

  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-​041
  (8973) Special Topics: Computer Graphics  Gizem Kayar TR 9:30-10:45AM 60 Fifth Ave 150
  

  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-​042
  (8974) Special Topics: Computer Vision  Davi Geiger TR 2:00-3:15PM GCASL 369
  

  Course Description:

  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-​051
  (8975) 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-​052
  (8976) Special Topics: Algorithmic Problem Solving  TBA MW 12:30-1:45PM 60 Fifth Ave 150
  

  Course Description:

  Student must also register for a Recitation section.

  Many of the top firms in the technological and financial sectors are using algorithmic problems as interview questions for assessing candidate skill. In this course we take this idea one step further and use algorithmic problem solving as way to hone programming skills. Students will use the material covered in the data structures and algorithms courses and learn new algorithmic techniques to solve challenging problems quickly. Each week will be devoted to a particular type of algorithm. Weekly problem sets will reinforce the lecture, and require students to implement their solutions in Java or C++.

  Students completing this course will be well prepared to participate in programming competitions such as the ACM Inter-Collegiate Programming Contest, TopCoder, and Google's Code Jam.

  
  
  Prerequisites: CSCI-UA.0310
• CSCI-UA.0480-​062
  (21315) Special Topics: Computer Networks  Jinkun Geng MW 11:00-12:15PM GCASL 361
  

  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-​063
  (8977) Special Topics: Introduction to Computer Security  Joseph Bonneau MW 2:00-3:15PM CIWW 109
  

  Course Description:

  This course will cover basic principles of computer security and security engineering. To facilitate understanding, this course will consider security both from an attacker's perspective (threat modeling) and the defender's perspective (building and deploying secure systems). Specific topics will include operating system security, network security, web security, and mobile device security. Course projects will focus both on writing secure code and exploiting insecure code.

  
  
  Prerequisites: CSCI-UA.0201
• CSCI-UA.0480-​076
  (22206) Special Topics: Technology, Law, and Policy  Sunoo Park MW 3:30-4:45PM CIWW 312
  

  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

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Classroom Locations