Course Schedule

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

View undergraduate course grid View graduate course grid

• CSCI-GA.1170-​003
  BMIN-GA.1170-001
  (5646) Fundamental Algorithms  Julia Kempe T 10:15-12:15PM Online
  

  Course Description:

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

  Required Foundational course for MSCS, MSIS

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.2110-​001
  (5650) Programming Languages  Joseph Tassarotti T 4:55-6:55PM Meyr 121
  

  Course Description:

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

  
  
  Prerequisites: Students taking this class should already have substantial programming experience.
• CSCI-GA.2112-​001
  MATH-GA 2043-001
  (5722) Scientific Computing   Sean Reiter TR 4:55-6:10PM 194M 203
  

  Course Description:

  Methods for numerical applications in the physical and biological sciences, engineering, and finance. Basic principles and algorithms; specific problems from various application areas; use of standard software packages.
  
  Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
• CSCI-GA.2437-​001
  (5720) Big Data Application Development  Yang Tang T 7:10-9:10PM Silv 208
  

  Course Description:

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

   

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

  
  
  Prerequisites: Students are required to successfully complete CSCI-GA 2436 Realtime and Big Data Analytics prior to taking this course. Students should have experience with Hadoop MapReduce and HDFS.
• CSCI-GA.2565-​001
  (5663) Machine Learning   Kyunghyun Cho T 4:55-6:55PM CANTR 101
  

  Course Description:

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

  
  
  Prerequisites:

  Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics, CSCI-GA.1180 Mathematical Techniques for CS Applications. 

• CSCI-GA.2630-​003
  (5708) Foundations of Networks and Mobile Systems*  Lakshminarayanan Subramanian T 10:15-12:15PM GCASL 375 Additional information: Update for Fall 2023, MSCS students can request a permission code by emailing graduate@cs.nyu.edu
  

  Course Description:

  The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Web­based systems, providing an introduction to data­center networks, clouds, middle boxes (proxies, firewalls) and next­ generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.
• CSCI-GA.2630-​004
  (5709) Foundations of Networks and Mobile Systems Lab*  Lakshminarayanan Subramanian T 12:30-1:20PM GCASL 375 Additional information: Lab for Section 003
  

  Course Description:

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

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

• CSCI-GA.2820-​001
  (5712) DevOps and Agile Methodologies  John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program
  

  Course Description:

  This course uses a project­ based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.

• CSCI-GA.2820-​002
  (5713) DevOps and Agile Methodologies Lab  John Rofrano T 7:10-8:00PM Online Additional information: Lab for Section 001. Open only to students in the Stern TECH MBA program.
  

  Course Description:

  This course uses a project­ based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.

• CSCI-GA.3033-​097
  (22335) Special Topics: Virtual Reality*  Kenneth Perlin T 7:10-9:10PM 60 Fifth Ave 446
  

  Course Description:

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

  To get permission to enroll, email Prof. Perlin at kp1@nyu.edu and forward the permission email to graduate@cs.nyu.edu for the permission code.

  
  
  Prerequisites: CSCI-GA.2270 Computer Graphics or CSCI-UA.0480-041 Computer Graphics. Permission of the instructor required.
• CSCI-GA.3033-​135
  (5697) Special Topics: Computational Genomics  Eran Halperin T 2:45-4:45PM 60 Fifth Ave 125
  

  Course Description:

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

  https://eran-halperin.github.io/CSCI-GA3033-135CompGenomics/

  
  
  Prerequisites: Python programming experience. Previous coursework in probability, statistics, and linear algebra. Familiarity with machine learning fundamentals is helpful but not required.
• CSCI-GA.3033-​140
  (5701) Special Topics: Honors Machine Learning  Gautam Kamath T 4:55-6:55PM Silv 401
  

  Course Description:

  This course covers fundamental topics in machine learning. Specific topics covered may include linear regression, logistic regression, k-nearest neighbor, support vector machines, kernels, decision trees, aggregation methods, dimensionality reduction, clustering, optimization, regularization, model evaluation, and more.
  
  Prerequisites: Undergraduate courses in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics, CSCI-GA.1180 Mathematical Techniques for CS Applications.
• CSCI-GA.3033-​141
  (5702) Special Topics: Theoretical Foundations in Modern Machine Learning  Allen Liu T 4:55-6:55PM Bobst LL138
  

  Course Description:

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

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

  Course Description:

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

  
  
  Prerequisites: CSCI-GA.2110 Programming Languages, CSCI-GA.3110 Honors Programming Languages
• CSCI-GA.3850-​001
  (5678) PhD Seminar: Cryptography **  Yevgeniy Dodis T 11:00-1:50PM CIWW 317 Additional information: This course also meets at CIWW 317. Please check with your instructor to confirm the schedule.
  

  Course Description:

  Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.
  
  Prerequisites: Permission of the instructor.
• CSCI-UA.0001-​001
  (21698) Computers & Society: Understanding AI: Technology Transforming Society  Alfredo Canziani TR 3:30-4:45PM 194M 208
  

  Course Description:

  This dynamic, team-taught course introduces students to the ideas and technologies that are redefining the future of Artificial Intelligence (AI). Turing Award winner Yann LeCun opens the course with the foundations of modern AI and concludes with a forward-looking exploration of the breakthroughs that lie ahead. Students learn from NYU’s top AI researchers, who guide them through the major subfields of the discipline, including machine learning, natural language processing, computer vision, robotics, and responsible AI. Throughout the course, students discover how AI is transforming industries around the world, with a special focus on innovations in healthcare and medicine, where reliability and human oversight are vital. This course offers an inspiring gateway into one of the most influential technologies of our time. The course spans 14 weeks, organized into 7 thematic modules, each lasting two weeks. Each module includes three lecture sessions (approximately 75 minutes each, delivered live) and one interactive session in the fourth meeting, featuring knowledge assessments (30 min) and hands-on activities (30 min). Weekly discussion sessions and an online forum provide opportunities for engagement with instructors. Assessment is based on weekly quizzes and participatory activities designed for non-technical audiences. canziani@nyu.edu
  
  Prerequisites: No prior computing experience is assumed. Not intended for computer science majors.
• CSCI-UA.0001-​003
  (21907) Computers and Society: Introduction to Computer Programming with AI Assistance  Matthew Zeidenberg TR 8:00-9:15AM 194M 206
  

  Course Description:

  An introduction to the fundamentals of computer programming and computational thinking. Students design, write, and debug programs while learning to collaborate effectively with artificial intelligence tools. The course emphasizes problem decomposition, system design, and the evaluation of code. No prior knowledge of programming or AI is assumed.
  
  Prerequisites: No prior computing experience is assumed. Not intended for computer science majors.
• CSCI-UA.0002-​002
  (8897) Intro To Computer Programming (No Prior Experience)  Julie Lizardo TR 8:00-9:15AM Online
  

  Course Description:

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​004
  (8899) Intro To Computer Programming (No Prior Experience)  TBA TR 9:30-10:45AM Bobst LL138
  

  Course Description:

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

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

  Course Description:

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0002-​014
  (24468) Intro To Computer Programming (No Prior Experience)  Michal Sadowski TR 11:00-12:15PM CIWW 312
  

  Course Description:

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

  
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with any programming experience should consult with the department before registering. Students with any reported score on the Computer Science AP examination cannot enroll in this course; Albert will block them from registering for it. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with no previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0003-​001
  (8984) Intro to Computer Programming (Limited Prior Experience)  Karl Rosenberg TR 12:30-1:45PM GCASL 275
  

  Course Description:

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

  
  
  Prerequisites: Limited programming experience, or any score on the AP Computer Science examination (students will forfeit the AP credit for scores of 4 or 5), or Data Science for Everyone (DS-UA 111). Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Does not count toward the computer science major; serves as the prerequisite for students with some previous programming experience who want to continue into CSCI-UA 101 and pursue the major. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3.
• CSCI-UA.0004-​002
  (8909) Intro to Web Design & Computer Principles  Adam Scher TR 8:00-9:15AM Online
  

  Course Description:

  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0004-​004
  (8911) Intro to Web Design & Computer Principles  Joshua Clayton TR 11:00-12:15PM 194M 208
  

  Course Description:

  Introduces students to both the practice of web design and the basic principles of computer science. The practice component covers not only web design but also current graphics and software tools. The principles section includes an overview of hardware and software, the history of computers, and a discussion of the impact of computers and the Internet.
  
  Prerequisites: Three years of high school mathematics or equivalent. No prior computing experience is assumed. Students with computing experience should consult with the department before registering.
• CSCI-UA.0060-​001
  (8915) Database Design And Implementation  Mark Ramos TR 11:00-12:15PM GCASL 361
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Students that have successfully completed Data Management and Analysis (CSCI-UA 479) are NOT eligible to take this course.
• CSCI-UA.0101-​004
  (8914) Intro To Computer Science  Gizem Kayar TR 11:00-12:15PM 194M 203
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0101-​006
  (8921) Intro To Computer Science  Candido Cabo TR 12:30-1:45PM Silv 208
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam (scores of 4 and 5 are acceptable prerequisites for students who wish to take CSCI-UA 101 before entering CSCI-UA 102; they will forfeit the AP credit), or departmental permission assessed by placement exam.
• CSCI-UA.0102-​040
  (8943) Data Structures  Karl Rosenberg TR 9:30-10:45AM CIWW 317 Additional information: In-Person Recitation for Section 3. Open to NYU Shanghai GoLocal students
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0102-​060
  (21314) Data Structures  Romain Cosson TR 3:30-4:45PM GCASL 261
  

  Course Description:

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

  
  
  Prerequisites: Introduction to Computer Science (CSCI-UA 101), or a score of 4 or 5 on the AP Computer Science examination.
• CSCI-UA.0201-​020
  (8949) Computer Systems Organization  Hasan Aljabbouli TR 9:30-10:45AM Online Online
  

  Course Description:

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

  
  
  Prerequisites: CSCI-UA.0102
• CSCI-UA.0201-​040
  (8953) Computer Systems Organization  Gizem Kayar TR 3:30-4:45PM 60 Fifth Ave 150 Additional information: Required Lecture is Section 41
  

  Course Description:

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

  
  
  Prerequisites: CSCI-UA.0102
• CSCI-UA.0202-​002
  (8956) Operating Systems  Mohamed Zahran TR 3:30-4:45PM Online Online
  

  Course Description:

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0310-​030
  (8961) Basic Algorithms  Mazdak Zamani TR 9:30-10:45AM 194M 206
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0310-​040
  (8963) Basic Algorithms  Rachit Garg TR 3:30-4:45PM 60 Fifth Ave C10
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and either Calculus I (MATH-UA 121) OR Mathematics for Economics I (MATH-UA 211).
• CSCI-UA.0467-​001
  (8986) Applied Internet Technology  Joseph Versoza TR 12:30-1:45PM Silv 520
  

  Course Description:

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201). Students who complete Applied Internet Technology (CSCI-UA 467) with a C or better are not eligible to take Web Development and Programming (CSCI-UA 61).
• CSCI-UA.0469-​001
  (8992) Natural Language Processing  Adam Meyers TR 8:00-9:15AM CIWW 101
  

  Course Description:

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0469-​002
  (8993) Natural Language Processing  Adam Meyers TR 3:30-4:45PM Silv 401
  

  Course Description:

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

  
  
  Prerequisites: Computer Systems Organization (CSCI-UA 201).
• CSCI-UA.0473-​001
  (8980) Fundamentals of Machine Learning  Sumit Chopra TR 12:30-1:45PM Cant 101
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) and Probability and Statistics (MATH-UA 185). Students may take any of the following courses in place of MATH-UA 185: Theory of Probability (MATH-UA 333), Mathematical Statistics (MATH-UA 334), Honors Theory of Probability (MATH-UA 338) OR Data Analysis (MA-UY 2224).
• CSCI-UA.0478-​001
  MATH-UA.0243
  (8972) Introduction To Cryptography  Benedikt Bünz TR 2:00-3:15PM GCASL 261
  

  Course Description:

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

  
  
  Prerequisites:

  Basic Algorithms (CSCI-UA 310).

• CSCI-UA.0479-​004
  (8991) Data Management and Analysis  Matthew Zeidenberg TR 2:00-3:15PM Silv 401
  

  Course Description:

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

  
  
  Prerequisites: Data Structures (CSCI-UA 102). Students that complete Data Management and Analysis (CSCI-UA 479) with a C or better are not eligible to take Database Design and Implementation (CSCI-UA 60).
• CSCI-UA.0480-​041
  (8973) Special Topics: Computer Graphics  Gizem Kayar TR 9:30-10:45AM 60 Fifth Ave 150
  

  Course Description:

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

  
  
  Prerequisites: CSCI-UA.0201
• CSCI-UA.0480-​042
  (8974) Special Topics: Computer Vision  Davi Geiger TR 2:00-3:15PM GCASL 369
  

  Course Description:

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

  
  
  Prerequisites: CSCI-UA.0201
• CSCI-UA.0480-​051
  (8975) Special Topics: Parallel Computing  Mohamed Zahran TR 2:00-3:15PM Online
  

  Course Description:

  Detailed course descriptions are available when advanced topics are announced each semester. Typical offerings include, but are not limited to, Bioinformatics, Building Robots, Computer Graphics, Machine Learning, Network Programming, Computer Vision, and Multimedia for Majors.

  
  
  Prerequisites: CSCI-UA.0201

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