Course Schedule

Current semester Fall 2024, and future semesters are now available to view.

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• CSCI-GA.1170-​001
  (7955) Fundamental Algorithms  Chee Yap T 4:55-6:55PM Meyr 121
  

  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
  (7956) Fundamental Algorithms Recitation  Bingwei Zhang M 5:55-6:45PM CIWW 109 Additional information: Recitation for Section 001
  

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​003
  (7957) Fundamental Algorithms  Ernest Davis T 4:55-6:55PM CIWW 312
  

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​006
  (7958) Fundamental Algorithms Recitation  Bingwei Zhang F 4:55-5:45PM Silv 206 Additional information: Recitation for Section 001
  

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.1170-​007
  (7959) Fundamental Algorithms Recitation  Sihyun Lee R 5:55-6:45PM 60 Fifth Ave 110
  

  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
  (7960) Mathematical Techniques For CS Applications  Parijat Dube W 7:10-9:10PM GCASL 275
  

  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
  (7961) Programming Languages  Joseph Tassarotti W 7:10-9:10PM Meyr 121
  

  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
  (7962) Programming Languages Recitation  Markus de Medeiros M 4:55-5:45PM CIWW 109
  

  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
  (7963) Programming Languages  Cory Plock M 4:55-6:55PM GCASL 383
  

  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
  (7964) Programming Languages Recitation  Hrithik Dhoka F 5:55-6:45PM GCASL 383
  

  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
  (7965) Programming Languages Recitation  Chaitanya Agarwal F 11:15-12:05PM Silv 520 Additional information: Recitation for Section 001
  

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

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2112-​001
  MATH-GA 2043-001
  (7967) Scientific Computing   Jonathan Goodman TR 4:55-6:10PM CIWW 1302
  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
  (7968) Operating Systems  Hubertus Franke M 7:10-9:10PM CIWW 109
  The topics covered include a review of linkers and loaders and the high-level design of key operating systems concepts such as process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging and segmentation; and I/O and file systems, with examples from Unix/Linux and Windows. Programming assignments may require C, C++, Java, or C#.
• CSCI-GA.2262-​001
  (7969) Data Communications and Networks  Jean-Claude Franchitti R 7:10-9:10PM Meyr 122
  

  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
  (7970) Computer Graphics   Kenneth Perlin W 7:10-9:10PM Bobst LL150
  

  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
  (7971) Computer Vision   Saining Xie R 7:10-9:10PM Silv 405
  

  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.2420-​001
  MATH-GA 2010-001
  (7972) Numerical Methods I   Benjamin Peherstorfer MW 4:55-6:10PM CIWW 1302
  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
  (7973) Database Systems  Jean-Claude Franchitti R 4:55-6:55PM Meyr 122
  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
  (7974) Advanced Database Systems  Dennis Shasha R 4:55-6:55PM CIWW 101
  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
  (8034) Realtime and Big Data Analytics  Yang Tang R 7:10-9:10PM CIWW 101
  

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

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

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

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

• CSCI-GA.2437-​001
  (8035) Big Data Application Development  Yang Tang T 7:10-9:10PM CIWW 101
  

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

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

  
  
  Prerequisites: Prerequisites include experience with Hadoop, which commonly comes from having completed the Realtime and Big Data Analytics course. There will be a review of Apache Hadoop, but students should have experience with Hadoop MapReduce and HDFS.
• CSCI-GA.2560-​001
  (7975) Artificial Intelligence   Paul Bethe M 4:55-6:55PM CIWW 101
  

  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
  (7976) Machine Learning   Mengye Ren T 4:55-6:55PM Silv 411
  

  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.2566-​001
  (7977) Foundations Of Machine Learning*  Mehryar Mohri T 4:55-6:55PM CIWW 317
  

  This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are probability and general bounds; PAC model; VC dimension; perceptron, Winnow; support vector machines (SVMs); kernel methods; decision trees; boosting; regression problems and algorithms; ranking problems and algorithms; halving algorithm, weighted majority algorithm, mistake bounds; learning automata, Angluin-type algorithms; and reinforcement learning, Markov decision processes (MDPs).

  
  
  Prerequisites: CSCI-GA 1180.
• CSCI-GA.2572-​001
  DS-GA.1008-001
  (8019) Deep Learning  Yann LeCun
  Alfredo Canziani W 4:55-6:55PM Meyr 121
  

  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
  (8020) Deep Learning Lab  Alfredo Canziani R 4:55-5:45PM Cant 101
  

  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.2630-​001
  (8021) Foundations of Networks and Mobile Systems*  CANCELLED -
  

  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-​002
  (8022) Foundations of Networks and Mobile Systems Lab*  CANCELLED -
  

  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-​003
  (8023) Foundations of Networks and Mobile Systems*  Lakshminarayanan Subramanian W 10:15-12:15PM 60 Fifth Ave C10
  

  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
  (8024) Foundations of Networks and Mobile Systems Lab*  Lakshminarayanan Subramanian W 12:30-1:20PM 60 Fifth Ave C10
  

  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
  (8027) Design and Innovation*  Gary Zamchick F 10:15-12:15PM CIWW 101
  

  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
  (8028) Design and Innovation Lab*  Gary Zamchick F 12:30-1:20PM CIWW 101
  

  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
  (8030) DevOps and Agile Methodologies  John Rofrano T 4:55-6:55PM Online
  

  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
  (8031) DevOps and Agile Methodologies Lab  John Rofrano T 7:10-8:00PM Online
  

  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
  (8032) DevOps and Agile Methodologies  John Rofrano W 4:55-6:55PM Online
  

  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
  (8033) DevOps and Agile Methodologies Lab  John Rofrano W 7:10-8:00PM Online
  

  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
  (20908) Entrepreneurship Capstone  Evan Korth M 10:15-12:15PM 194M 304
  

  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
  (20927) Entrepreneurship Capstone Lab  Charlie O'Donnell M 12:30-1:20PM 194M 306B
  

  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
  (7980) Heuristic Problem Solving  Dennis Shasha R 7:10-9:10PM CIWW 312
  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
  (8000) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming  Mohamed Zahran W 4:55-6:55PM 60 Fifth Ave 150
  

  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.

• CSCI-GA.3033-​029
  (22227) Special Topics: Social Networks  Bhubaneswar Mishra R 4:55-6:55PM CIWW 202
  

  Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. The World Wide Web enables information flows among vast number of humans; Facebook, and LinkedIn, etc. connect small groups of friends; amazon, eBay, etc. provide opportunities for trading, etc, BitCoins, Ethereum, etc. allow parties to make financial and contractual transactions . These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorumsensing, swarming, flocking, social grooming, gossip, etc., and have evolved with the dynamics of evolutionary games (particularly Signaling Games). Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant. Economic markets also look much more like networks than anonymous marketplaces. Firms interact with the suppliers and customers in a Web-like supply chains. Systemic risk in financial markets often results from the counterparty risks created within this financial network. This course will introduce the tools for the study of networks, and mechanism designed to interact strategically: SmartContractss, Costly Signaling, Credible and Noncredible Threats, Model Checking and Verification, Safety and Liveness, Recommenders and Verifiers, Deception, Market mechanisms (pricing and auction), Privacy and Trust… Exampled Systems: Bit-coins, Zcash, Ripple, Stellar, Ethereum, Solidity, Rholang, DAO (Distributed Companies), Cyber Security (M-coins), Information Finance Markets (WealthFront, etc.), Market Microstructure, ICO/STO/ITO, GDPR, OpenLaw, OpenZeplin, ...

• CSCI-GA.3033-​034
  (8001) Special Topics: Multicore Processors: Architecture & Programming  Mohamed Zahran T 4:55-6:55PM CIWW 101
  

  This course serves as a capstone for the MSCS program.
  
  The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable. 

• CSCI-GA.3033-​061
  (8002) Special Topics: Predictive Analytics  Anasse Bari W 4:55-6:55PM 60 Fifth Ave 110
  

  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.

• CSCI-GA.3033-​085
  (8004) Special Topics: Cloud and Machine Learning  I-Hsin Chung
  Hao Yu R 4:55-6:55PM GCASL 369
  

  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.

• CSCI-GA.3033-​087
  DS-GA.3001-013
  (8067) Special Topics in Data Science: Bayesian Machine Learning  Andrew Gordon Wilson M 10:00-11:40AM GCASL 383
  

  This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will comprise three units, alongside a major research project component. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization. 3. Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference.

  
  
  Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning (2006) before class starts. Prior courses on machine learning are strongly recommended.
• CSCI-GA.3033-​091
  (8006) Special Topics: Introduction to Deep Learning Systems  Parijat Dube
  Chen Wang T 7:10-9:10PM Silv 414
  

  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.

• CSCI-GA.3033-​111
  (8010) Special Topics: Protein Design  Rob Fergus M 7:10-9:10PM 12WP L120
  

  This new course will explore recent exciting developments in the application of machine learning and computational methods to the design of new-to-nature proteins. These advances promise a revolution in drug design, scientific tooling, materials and more. The course will consist of a mix of lectures (given by the instructor and external speakers) and seminar-style reading groups of recent papers. Assessment will be through class participation (discussions & paper presentations). 

  
  
  Prerequisites: Graduate-level machine learning class. Some knowledge of molecular biology may be useful, although not essential.
• CSCI-GA.3033-​112
  (8017) Special Topics: Fair and Ethical Machine Learning for Social Good  Daniel Neill M 10:15-12:15PM GCASL 265
  

  Machine learning (ML) is increasingly used to inform high-stakes decisions that impact individuals' lives and livelihoods. For example, ML algorithms may determine who is given a loan, hired for a job, or sentenced to prison, or how scarce resources are allocated by a hospital or a government agency. It is critical to ensure that these decisions are made in a fair, responsible, and ethical manner, both maximizing the societal benefits and minimizing the harms of ML in practice. The first half of the course will focus primarily on fair ML: defining fairness, building fairer models, and auditing algorithms to identify and correct biases. Fair ML methods, embedded in a broader socio-technical perspective, can mitigate the risks that ML algorithms will create inequity and injustice or that they will exacerbate existing societal disparities. Moreover, fair ML can benefit society by correcting systematic biases in human decisions and by identifying and reducing societal disparities. The second half of the course will consider numerous other aspects of ML ethics (reliability and trustworthiness; privacy and security; interpretability and explainability; transparency and accountability), and will discuss practical challenges and solutions for measuring and optimizing the societal impacts of deployed ML systems. The course will include both lectures on core content and discussion of current research papers. Deliverables will include paper presentations, assignments (including Python coding), and a substantial, semester-long research project.

  
  
  Prerequisites: A graduate or advanced undergraduate course in machine learning; Python programming including numpy, pandas, and scikit-learn.
• CSCI-GA.3033-​113
  (23379) Special Topics: Applied Technical Foundations for Managers  Taylor Valore T 9:00-11:00AM KMEC 2-60 Additional information: Open only to students in the Stern Tech-Focused MBA program
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​187
  (8068) Special Topics in Data Science: Bayesian Machine Learning Lab  Andres Potapczynski Guiza R 12:30-1:20PM Silv 414
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​925
  (8013) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Recitation  Gokulkrishna Muthusamy F 11:15-12:05PM Meyr 122
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​934
  (8014) Special Topics: Multicore Processors: Architecture & Programming Recitation  Karan Gangadhar F 12:30-1:20PM Silv 401
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3033-​9113
  (23380) Special Topics: Applied Technical Foundations for Managers Recitation  Taylor Valore T 11:05-11:55AM KMEC 2-60 Additional information: Open only to students in the Stern Tech-Focused MBA program
  

  Topics vary each semester.

  
  
  Prerequisites: Prerequisites vary according to topic.
• CSCI-GA.3110-​001
  (7981) Honors Programming Languages  Patrick Cousot TR 3:30-4:45PM 60 Fifth Ave 110
  

  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: <p>CSCI-GA.2110 Programming Languages&nbsp;<b>or</b>&nbsp;CSCI-GA.3110 Honors Programming Languages</p>
• CSCI-GA.3205-​001
  (7982) Applied Cryptography & Network Security  Mazdak Zamani T 7:10-9:10PM 60 Fifth Ave 110
  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
  (7983) Introduction To Cryptography  Marshall Ball R 7:10-9:10PM GCASL 288
  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
  (9022) Honors Analysis of Algorithms  Subhash Khot TR 6:20-7:25PM CIWW 102
  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
  (8018) Information Technology Projects *  Jean-Claude Franchitti M 6:10-9:10PM 60 Fifth Ave 110
  

  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
  (7984) Adv Lab MS *  STAFF -
  

  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
  (7986) Adv Lab MS *  STAFF -
  

  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
  (7987) Adv Lab MS *  STAFF -
  

  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
  (7988) Adv Lab MS *  STAFF -
  

  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
  (7989) Adv Lab PhD **  STAFF -
  

  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
  (7990) Adv Lab PhD **  STAFF -
  

  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
  (7991) Adv Lab PhD **  STAFF -
  

  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
  (7992) Adv Lab PhD **  STAFF -
  

  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
  (7993) Master's Thesis Research *  STAFF -
  
  
  Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
• CSCI-GA.3850-​001
  (7994) PhD Seminar: Cryptography**  Yevgeniy Dodis W 1:00-3:00PM CIWW 412
  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-GA.3850-​004
  (7995) PhD Seminar: Formal Methods**  Thomas Wies R 11:00-12:30PM 60 Fifth Ave 446
  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-GA.3860-​001
  (7997) PhD Thesis Research **  STAFF -
  
  
  Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
• CSCI-GA.3870-​001
  (7998) Internship In Computer Science MS*  STAFF -
  

  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
  (7999) Internship in Computer Science PhD*  STAFF -
  

  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.0002-​001
  (6854) Intro To Computer Programming (No Prior Experience)  Amanda Steigman MW 8:00-9:15AM Tisch LC11
  

  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
  (6855) Intro To Computer Programming (No Prior Experience)  Susan Liao TR 8:00-9:15AM Bobst LL150
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​003
  (6856) Intro To Computer Programming (No Prior Experience)  Waqar Aqeel MW 9:30-10:45AM CIWW 317
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​004
  (6857) Intro To Computer Programming (No Prior Experience)  Nathan Miranda TR 9:30-10:45AM GCASL 261
  

  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
  (6858) Intro To Computer Programming (No Prior Experience)  Emily Zhao MW 11:00-12:15PM GCASL 261
  

  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
  (6859) Intro To Computer Programming (No Prior Experience)  David Prager TR 11:00-12:15PM GCASL 369
  

  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
  (6860) Intro To Computer Programming (No Prior Experience)  Emily Zhao MW 12:30-1:45PM GCASL 275
  

  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
  (6861) Intro To Computer Programming (No Prior Experience)  David Prager TR 12:30-1:45PM Tisch LC11
  

  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
  (6862) Intro To Computer Programming (No Prior Experience)  Adam Meyers MW 2:00-3:15PM Kimmel 808
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​010
  (6883) Intro To Computer Programming (No Prior Experience)  Matthew Zeidenberg TR 2:00-3:15PM Silv 520
  

  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-​011
  (6864) Intro To Computer Programming (No Prior Experience)  Joshua Clayton MW 3:30-4:45PM CIWW 109
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students 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-​012
  (6865) Intro To Computer Programming (No Prior Experience)  Khye Borg Liew TR 3:30-4:45PM Online
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​013
  (6866) Intro To Computer Programming (No Prior Experience)  Na'im Tyson MW 8:00-9:15AM GCASL 388
  

  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-​014
  (6867) Intro To Computer Programming (No Prior Experience)  Karl Rosenberg MW 9:30-10:45AM GCASL 388
  

  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-​015
  (6868) Intro To Computer Programming (No Prior Experience)  Craig Kapp MW 3:30-4:45PM CIWW 317
  

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0003-​001
  (6947) Intro to Computer Programming (Limited Prior Experience)  Adam Meyers TR 11:00-12:15PM 19UP 102
  

  Introduces object-oriented programming, recursion, and other important concepts to students who already have had some exposure to programming in the context of building applications using Python. Students will design and implement Python programs in a variety of applied areas.

  
  
  Prerequisites: Limited programming experience, or any score on the AP Computer Science examination (students will forfeit the AP credit for scores of 4 or 5), or Data Science for Everyone (DS-UA 111). Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with some previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0004-​001
  (6869) Intro to Web Design & Computer Principles  Alvaro Olsen MW 8:00-9:15AM CIWW 101
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​002
  (6870) Intro to Web Design & Computer Principles  Adam Scher TR 8:00-9:15AM Online
  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​003
  (6871) Intro to Web Design & Computer Principles  Mark Ramos MW 11:00-12:15PM CIWW 202
  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
  (6872) Intro to Web Design & Computer Principles  Joshua Clayton TR 11:00-12:15PM CIWW 202
  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
  (6873) Intro to Web Design & Computer Principles  Alvaro Olsen MW 3:30-4:45PM CIWW 312
  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
  (6874) Intro to Web Design & Computer Principles  Emily Zhao TR 3:30-4:45PM GCASL 279
  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
  (6875) Database Design And Implementation  David Prager MW 2:00-3:15PM CIWW 202
  

  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
  (6876) Web Development And Programming  Sana Odeh TR 3:30-4:45PM CIWW 101
  

  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
  (6877) Intro To Computer Science  CANCELLED -
  

  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
  (6878) Intro To Computer Science  Gabriel Provencher Langlois TR 8:00-9:15AM GCASL 383
  

  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
  (6879) Intro To Computer Science  Tobias Blickhan MW 9:30-10:45AM 60 Fifth Ave C15
  

  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
  (6880) Intro To Computer Science  Sana Odeh MW 11:00-12:15PM CIWW 317
  

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

  
  
  Prerequisites: Introduction to Computer Programming (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
  (6881) Intro To Computer Science  Gizem Kayar TR 11:00-12:15PM CIWW 312
  

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​006
  (6882) Intro To Computer Science  Hilbert Gene Locklear MW 12:30-1:45PM Silv 101A
  

  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
  (6883) Intro To Computer Science  Candido Cabo TR 12:30-1:45PM CIWW 101
  

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​008
  (6884) Intro To Computer Science  Sana Odeh MW 2:00-3:15PM CIWW 101
  

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​009
  (6885) Intro To Computer Science  Anasse Bari TR 2:00-3:15PM CIWW 101
  

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​010
  (6886) Intro To Computer Science  Michael Tao MW 3:30-4:45PM GCASL 279
  

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​011
  (6887) Intro To Computer Science  Daniel Zint TR 3:30-4:45PM CIWW 202
  

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

  
  
  Prerequisites: Introduction to Computer Programming (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-​001
  (6888) Data Structures  Evan Korth TR 12:30-1:45PM Cant 102
  

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​002
  (6889) Data Structures - Recitation  Erik Larsen F 8:00-9:15AM 60 Fifth Ave C15 Additional information: In-person recitation for Section 1
  

  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-​003
  (6890) Data Structures  Anasse Bari TR 11:00-12:15PM CIWW 101
  

  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-​004
  (6891) Data Structures - Recitation  Yvonne Wu F 9:30-10:45AM CIWW 202 Additional information: In-person recitation for Section 3
  

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​005
  (6892) Data Structures  Joanna Klukowska MW 8:00-9:15AM CIWW 109
  

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​006
  (6893) Data Structures - Recitation  Satya Chillale F 8:00-9:15AM CIWW 317 Additional information: In-person recitation for Section 5
  

  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-​007
  (6894) Data Structures  Max Sklar MW 11:00-12:15PM CIWW 312
  

  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-​008
  (6895) Data Structures - Recitation  Aman Gupta F 11:00-12:15PM Bobst LL138 Additional information: In-person recitation for Section 7
  

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​009
  (6896) Data Structures  Sungmin Cha MW 12:30-1:45PM 60 Fifth Ave 110
  

  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-​010
  (6897) Data Structures - Recitation  Sanam Palsule F 12:30-1:45PM 194M 204
  

  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
  (6898) Data Structures  Alan Amin MW 2:00-3:15PM 60 Fifth Ave 110
  

  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
  (6899) Data Structures - Recitation  Charlie Chen F 2:00-3:15PM Bobst LL150 Additional information: In-person recitation for Section 11
  

  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
  (6900) Data Structures - Recitation  Erik Larsen F 9:30-10:45AM 60 Fifth Ave C15 Additional information: In-person recitation for Section 1
  

  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
  (6901) Data Structures - Recitation  Priyanka Agrahari F 11:00-12:15PM 60 Fifth Ave C15 Additional information: In-person recitation for Section 1
  

  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
  (6902) Data Structures - Recitation  Priyanka Agrahari F 12:30-1:45PM 60 Fifth Ave C15 Additional information: In-person recitation for Section 1
  

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​024
  (6903) Data Structures - Recitation  Aman Boobna F 2:00-3:15PM 60 Fifth Ave C15 Additional information: In-person recitation for Section 1
  

  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-​025
  (6904) Data Structures - Recitation  Aman Boobna F 3:30-4:45PM 60 Fifth Ave C15 Additional information: In-person recitation for Section 1
  

  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
  (6905) Data Structures - Recitation  Kartik Kanotra F 11:00-12:15PM CIWW 202 Additional information: In-person recitation for Section 3
  

  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
  (6906) Data Structures - Recitation  Satya Chillale F 9:30-10:45AM CIWW 317 Additional information: In-person recitation for Section 5
  

  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-​062
  (6907) Data Structures - Recitation  Daniel Jin F 11:00-12:15PM CIWW 317 Additional information: In-person recitation for Section 5
  

  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-​063
  (6908) Data Structures - Recitation  Yanchong Xu F 12:30-1:45PM CIWW 317 Additional information: In-person recitation for Section 5
  

  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-​064
  (6909) Data Structures - Recitation  Chenfeiyu Wen F 2:00-3:15PM CIWW 317 Additional information: In-person recitation for Section 5
  

  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-​065
  (6910) Data Structures - Recitation  Daniel Jin F 3:30-4:45PM CIWW 317 Additional information: In-person recitation for Section 5
  

  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-​001
  (6911) Computer Systems Organization  Douglas Moody MW 9:30-10:45AM CIWW 101
  

  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-​002
  (6912) Computer Systems Organization - Recitation  Anway Agte F 9:30-10:45AM GCASL 383 Additional information: In-person recitation for Section 1
  

  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-​003
  (6913) Computer Systems Organization  Hasan Aljabbouli TR 9:30-10:45AM Online
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​004
  (6914) Computer Systems Organization - Recitation  Arsalan Anwar F 9:30-10:45AM Online Additional information: Online Recitation for Section 3
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​005
  (6915) Computer Systems Organization  Benjamin Goldberg MW 3:30-4:45PM Silv 405
  

  This section is a fast paced version of the regular CSO class. Students who are considering research in their senior year or graduate school should take this class.
  
  This course aims to give students an understanding of what's "under the hood" in a modern computer system. Take this class to boost your confidence on a computer and to understand how everything works like never before! We'll cover basic topics including how machines represent and execute programs, how user program interact with the OS, as well as advanced topics including how to write networked and concurrent programs. These topics are supported by a series of challenging lab assignments in C.

  
  
  Prerequisites: You need to be comfortable with programming (e.g. in Java) and the UNIX environment.
• CSCI-UA.0201-​006
  (6916) Computer Systems Organization - Recitation  Anway Agte F 2:00-3:15PM Silv 405
  

  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-​007
  (6917) Computer Systems Organization  Gizem Kayar TR 3:30-4:45PM 60 Fifth Ave 150
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102).
• CSCI-UA.0201-​008
  (6918) Computer Systems Organization - Recitation  Bhairavi Sawantdesai F 2:00-3:15PM Silv 408 Additional information: In-person recitation for Section 7
  

  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
  (6919) Operating Systems  Yang Tang TR 3:30-4:45PM Online
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0202-​002
  (6920) Operating Systems  Michael Walfish MW 11:00-12:15PM CIWW 109
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0202-​003
  (22496) Operating Systems  Jocelyn Chen TR 11:00-12:15PM 7E12 LL23
  

  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-​001
  (6921) Basic Algorithms  Rotem Oshman MW 9:30-10:45AM CIWW 109
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​002
  (6922) Basic Algorithms - Recitation  Eli Goldin F 11:00-12:15PM 194M 203 Additional information: In-person recitation for Section 1
  

  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-​003
  (6923) Basic Algorithms  Alan Siegel MW 2:00-3:15PM Online
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​004
  (6924) Basic Algorithms - Recitation  Alan Siegel F 11:00-12:15PM Online Additional information: Online Recitation for Section 3
  

  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-​007
  (6925) Basic Algorithms  Jiaxin Guan TR 9:30-10:45AM CIWW 312
  

  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-​008
  (6926) Basic Algorithms - Recitation  Jiaming Li F 9:30-10:45AM Bobst LL138 Additional information: In-person recitation for Section 7
  

  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
  (6927) Basic Algorithms - Recitation  Rachit Garg F 12:30-1:45PM GCASL 275 Additional information: In-person recitation for Section 1
  

  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-​022
  (6928) Basic Algorithms - Recitation  Rachit Garg F 2:00-3:15PM GCASL 388 Additional information: In-person recitation for Section 1
  

  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
  (6929) Topics of General Interest: Interactive Computing  Craig Kapp MW 2:00-3:15PM CIWW 312
  

  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 and CSCI-UA.0004, or permission of Instructor
• CSCI-UA.0381-​001
  (6949) Programming Tools for the Data Scientist  Hasan Aljabbouli MW 2:00-3:15PM CIWW 317
  

  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.0467-​001
  (6950) Applied Internet Technology  Joseph Versoza TR 9:30-10:45AM CIWW 101
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201). Students who complete Applied Internet Technology (CSCI-UA 467) with a C or better are not eligible to take Web Development and Programming (CSCI-UA 61).
• CSCI-UA.0467-​002
  (6951) Applied Internet Technology  Joseph Versoza MW 12:30-1:45PM CIWW 109
  

  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
  (6955) Natural Language Processing  Adam Meyers TR 2:00-3:15PM 12WV G08
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0470-​001
  (6930) Object Oriented Programming  Hasan Aljabbouli MW 3:30-4:45PM CIWW 101
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0472-​001
  (6931) Artificial Intelligence   Ernest Davis MW 11:00-12:15PM Online
  Many cognitive tasks that people can do easily and almost unconsciously have proven extremely difficult to program on a computer. Artificial intelligence tackles the problem of developing computer systems that can carry out these tasks. Focus is on three central areas in AI: representation and reasoning, machine learning, and natural language processing.
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201) and Basic Algorithms (CSCI-UA 310).
• CSCI-UA.0473-​001
  (6943) Fundamentals of Machine Learning  Joan Bruna TR 2:00-3:15PM Meyr 121
  

  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.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0473-​002
  (6944) Fundamentals of Machine Learning  Zhe Zeng F 9:30-10:45AM 60 Fifth Ave 150 Additional information: In-person recitation for Section 1
  

  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.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0473-​021
  (6945) Fundamentals of Machine Learning  Zhe Zeng F 2:00-3:15PM Silv 206 Additional information: In-person recitation for Section 1
  

  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.235). Students may take any of the following courses in place of MATH-UA.235: Theory of Probability (MATH-UA 233), Mathematical Statistics (MATH-UA 234), Honors Theory of Probability (MATH-UA 238) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0474-​001
  (6932) Software Engineering  Amos Bloomberg MW 2:00-3:15PM Online
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0475-​001
  (23703) Predictive Analytics  Ahmad Emad MW 8:00-9:15AM WWH 312
  

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA 310), and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148).
• CSCI-UA.0476-​001
  (6948) Processing Big Data for Analytics Applications  CANCELLED -
  

  Introduces platforms, tools, and architectures that facilitate scalable management and processing of vast quantities of data. Explores open source tools enabling the efficient acquisition, distributed storage, and processing of big data. Provides hands-on experience with distributed processing Apache solutions such as Hadoop MapReduce, HBase, Hive, Impala, Pig, core Spark, Spark SQL, and Spark Streaming. Other Apache big data tools covered are Oozie, Zookeeper, Flume, and Kafka.

  
  
  Prerequisites: (CSCI-UA 201) Computer Systems Organization and (CSCI-UA 310) Basic Algorithms. Familiarity with Linux commands and SQL will be helpful but is not required.
• CSCI-UA.0479-​001
  (6952) Data Management and Analysis  Joseph Versoza MW 11:00-12:15PM CIWW 101
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0479-​002
  (6953) Data Management and Analysis  Matthew Zeidenberg TR 11:00-12:15PM Kimmel 808
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0479-​003
  (6954) Data Management and Analysis  Matthew Zeidenberg MW 3:30-4:45PM 60 Fifth Ave 150
  

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0480-​041
  (6933) Special Topics: Computer Graphics  Gizem Kayar TR 9:30-10:45AM 60 Fifth Ave 150
  

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

  
  
  Prerequisites: CSCI-UA 201 Computer Systems Organization, CSCI-UA-310 (Basic Algorithms) and MATH-UA-140 (Linear Algebra), or departmental consent.
• CSCI-UA.0480-​042
  (6934) Special Topics: Computer Vision  Davi Geiger TR 2:00-3:15PM CIWW 317
  

  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: Computer System Organization (CSCI-UA.0201) and Linear Algebra (CSCI-UA 140) with a grade of C or better
• CSCI-UA.0480-​046
  (22149) Special Topics: Intro to Social Networking  Bhubaneswar Mishra TR 2:00-3:15PM CIWW 202
  

  Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. Their utilities have been enhanced by their ability to generate massive amount of personal data that need to be analyzed and disseminated quickly. The World Wide Web enables information flows among vast number of humans; Facebook, LinkedIn, etc. connect small groups of friends; amazon, eBay, etc. provide opportunities for trading, etc. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorum-sensing, swarming, flocking, social grooming, gossip, etc. Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant. This course will introduce the tools, analytics and algorithms for the study of networks and their data. It will show how certain common principles permeate the functioning of these diverse networks: e.g., issues related to robustness, fragility, and interlinkages etc.

  
  
  Prerequisites: Computer System Organization (CSCI-UA.0201) with a grade of C or better
• CSCI-UA.0480-​051
  (6935) Special Topics: Parallel Computing  Mohamed Zahran TR 2:00-3:15PM Online
  

  A microprocessor with a single core no longer exists in almost all computers we are using today (including your tablet, smartphone, and even smartwatch). Most of our devices are now multicore processors. A multicore processor contains several cores (called CPUs or cores) on-chip. There are also accelerator chips, like GPUs. To make the best use of these multicore chips, we need to program them in-parallel. Sequential programming, for all platforms from smartphones to supercomputers, is falling out of fashion and taking a back-seat to parallel programming. How to think in parallel? How to write code in parallel to make the best use of the underlying hardware? How is this hardware different from the traditional one? What will the future be for software and hardware? This is what this course is about.

  
  
  Prerequisites: Computer System Organization (CSCI-UA.0201) with a grade of C or better
• CSCI-UA.0480-​052
  (6936) Special Topics: Algorithmic Problem Solving  Joanna Klukowska MW 12:30-1:45PM Silv 520
  

  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.0201 Computer Systems Organization, CSCI-UA.0310 Basic Algorithms. Student must also register for a Recitation section.
• CSCI-UA.0480-​055 Special Topics: Principles of Programming Languages  CANCELLED -
  

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

  
  
  Prerequisites: CSCI-UA.0201 with a grade of C or better
• CSCI-UA.0480-​063
  (6938) Special Topics: Introduction to Computer Security  Benedikt Bünz MW 2:00-3:15PM CIWW 109
  

  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 201 Computer Systems Organization with a grade of C or better. Recommended: Operating Systems (CSCI-UA.202)
• CSCI-UA.0480-​069
  (6939) Special Topics: Agile Software Development and DevOps  Amos Bloomberg TR 12:30-1:45PM Online
  

  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, an Agile methodology and DevOps automation have increased the speed, robustness, and scalability with which software is developed today and have reduced the uncertainty of outcomes inherent in complex software development. Upon completion of this course, students will understand the core methodologies, technologies, and tools used in the software industry today.

  
  
  Prerequisites: CSCI-UA.0201 with a grade of C or better
• CSCI-UA.0480-​073
  (6940) Special Topics: Introduction to Robot Intelligence  Lerrel Pinto TR 3:30-4:45PM 60 Fifth Ave C15
  

  This course will look at randomization as a tool for designing more efficient algorithms. Examples include primality testing, string matching, stable matching with random preferences, parallel quicksort, hashing, bloom filters. The course will cover the necessary background in probability.

  
  
  Prerequisites: CSCI-UA.310 Basic Algorithms and CSCI-UA.201 Computer Systems Organization

• *
  Indicates controlled enrollment (permission number required for registration). Contact your program advisor.
• **
  Indicates controlled enrollment (permission number required for registration). Contact Santiago Pizzini (pizzini@cs.nyu.edu).
• ***
  Indicates controlled enrollment (permission number required for registration). Contact the instructor.

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Classroom Locations

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