Course Schedule

Current semester Spring 2019, and future semesters are now available to view.

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• CSCI-GA.1170-​001
  DS-GA.1170
  (2633) Fundamental Algorithms  Alexander Alekseyev
  Office Hours M 5:10-7:00PM Silv 405
  

  Course Description:

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

  
  
  Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
• CSCI-GA.2130-​001
  (2637) Compiler Construction  Eva Rose
  Office Hours M 7:10-9:00PM CIWW 202
  

  Course Description:

  This is a capstone course based on compilers and modern programming languages. The topics covered include structure of one-pass and multiple-pass compilers; symbol table management; lexical analysis; traditional and automated parsing techniques, including recursive descent and LR parsing; syntax-directed translation and semantic analysis; run-time storage management; intermediate code generation; introduction to optimization; and code generation. The course includes a special compiler-related capstone project, which ties together concepts of algorithms, theory (formal languages), programming languages, software engineering, computer architecture, and other subjects covered in the MS curriculum. This project requires a substantial semester-long programming effort, such as construction of a language compilation or translation system that includes lexical and syntactic analyzers, a type checker, and a code generator.
  
  Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
• CSCI-GA.2250-​001
  (2638) Operating Systems  Hubertus Franke
  Office Hours M 7:10-9:00PM CIWW 109
  

  Course Description:

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

  
  
  Prerequisites: None
• CSCI-GA.2440-​001
  (2642) Software Engineering   Jean-Claude Franchitti
  Office Hours M 7:10-9:00PM CIWW 317
  

  Course Description:

  This is a capstone course focusing on large-scale software development. This course presents modern software engineering techniques and examines the software life cycle, including software specification, design, implementation, testing, and maintenance. Object-oriented design methods are also considered. Software engineering projects involve creation of a large-scale software system and require 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.2560-​001
  (3538) Artificial Intelligence   Ernest Davis
  Office Hours M 5:10-7:00PM CIWW 102
  

  Course Description:

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

• CSCI-GA.2572-​001
  DS-GA.1008-001
  (23069) Deep Learning  Yann LeCun M 4:55-6:35PM CIWW 109
  

  Course Description:

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

  
  
  Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
• CSCI-GA.2945-​001
  MATH-GA.2012-001
  (2644) Adv Topics Num Analysis: High Performance Computing  Georg Stadler
  Dhairya Malhotra M 5:10-7:00PM CIWW 1302
  

  Course Description:

  This class will be an introduction to the fundamentals of parallel scientific computing. We will establish a basic understanding of modern computer architectures (CPUs and accelerators, memory hierarchies, interconnects) and of parallel approaches to programming these machines (distributed vs. shared memory parallelism: MPI, OpenMP, OpenCL/CUDA). Issues such as load balancing, communication, and synchronization will be covered and illustrated in the context of parallel numerical algorithms. Since a prerequisite for good parallel performance is good serial performance, this aspect will also be addressed. Along the way you will be exposed to important tools for high performance computing such as debuggers, schedulers, visualization, and version control systems. This will be a hands-on class, with several parallel (and serial) computing assignments, in which you will explore material by yourself and try things out. There will be a larger final project at the end. You will learn some Unix in this course, if you don't know it already. Prerequisites for the course are (serial) programming experience with C/C++ (I will use C in class) or FORTRAN, and some familiarity with numerical methods.

• CSCI-GA.2945-​002
  MATH-GA.2012-002
  (2645) Adv Topics Num Analysis: Immersed Boundary Method  Charles Peskin M 11:00-12:50PM CIWW 1302
  

  Course Description:

  The immersed boundary (IB) method is a general framework for the computer simulation of flows with immersed elastic boundaries and/or complicated geometry. It was originally developed to study the fluid dynamics of heart valves, and it has since been applied to a wide variety of problems in biofluid dynamics, such as wave propagation in the inner ear, blood clotting, fish swimming, and insect flight.

  Non-biological applications include sails, parachutes, flows of suspensions, and two-fluid or multifluid problems. Topics to be covered include: mathematical formulation of fluid-structure interaction in Eulerian and Lagrangian variables with the Dirac delta function as the link between these two kinds of variables; discretization of the structure, fluid, and interaction equations, including energy-based discretization of the structure equations, finite difference discretization of the fluid equations, and IB delta functions with specified mathematical properties; an IB method with an exactly divergence-free interpolated velocity field; IB methods for immersed boundaries with non-trivial mass and for fluids with non-uniform density and viscosity; IB methods for immersed filaments with bend and twist; and a stochastic IB method for thermally fluctuating hydrodynamics within biological cells.

  Course requirements include homework assignments and a computing project, but no exam. Students may collaborate on the homework and on the computing project, and are encouraged to present the results of their computing projects to the class.

  Text:

  The Immersed Boundary Method, lecture notes by Charles S. Peskin. These notes will be supplemented by selected publications on the topics of the course.

  
  
  Prerequisites: Familiarity with numerical methods and fluid dynamics.
• CSCI-GA.2945-​004
  MATH-GA.2012-004
  (24010) Adv Topics Num Analysis: Stochastic modeling and uncertainity quantification in complex systems  Benjamin Peherstorfer
  Office Hours M 1:25-3:15PM CIWW 517
  

  Course Description:

  As physical and time domains of partial differential equations (PDEs) can be resolved with ever higher accuracy, the quality of numerical simulations of physical phenomena increasingly tends to be limited by noisy and incomplete data that insufficiently describe boundary conditions, coefficients, and other parameters of the problem setups. This course provides an introduction to the stochastic modeling of these data uncertainties as random coefficients and random forcing terms of PDEs and discusses dimensionality reduction methods for efficiently estimating moments of the solutions of the corresponding stochastic PDEs. The first part focuses on stochastic modeling and sampling (Karhunen-Loeve expansion, polynomial chaos expansion, stochastic Galerkin, (multilevel) Monte Carlo, stochastic collocation, sparse grids). The second part discusses dimensionality reduction techniques to make estimation of moments of the PDE solutions computationally tractable (proper orthogonal decomposition, a posteriori error estimation and error control of reduced models, empirical interpolation).

• CSCI-GA.3033-​008
  (3136) Special Topics: .Net Web Application Development  Cory Plock
  Office Hours M 5:10-7:00PM CIWW 101
  

  Course Description:

  This course introduces the concepts and tools necessary for building robust web applications using the Microsoft .NET Framework. Among the topics explored will be the multi-language .NET architecture, build and deployment process, the C# programming language, Entity Framework, Model-View-Controller (MVC) design pattern, web services, Git version control, and the Azure cloud platform. This is a hands-on project-based course where students will build a fully functional web solution.

  
  
  Prerequisites: Programming languages, object-oriented programming, relational databases, and data structures.
• CSCI-GA.3033-​020
  DS-GA.3001-014
  (3357) Special Topics: Mathematics of Deep Learning  Joan Bruna M 7:10-9:00PM CIWW 102
  

  Course Description:

  This graduate-level course explores some of the open mathematical challenges arising in the study of Deep Learning. In particular, we will focus on three main topics: (i) signal processing principles underpinning convolutional neural networks and their generalisations to non-euclidean geometries (ii) non-convex optimization challenges arising in high-dimensional (or overparametrised) learning problems, and (iii) geometry of Markov Decision Processes and its role in Reinforcement Learning guarantees.

  
  
  Prerequisites: Background on high-dimensional probability, statistics, and/or harmonic analysis. Familiarity with general Machine Learning topics and basic notions of optimization.
• CSCI-GA.3033-​024
  (20911) Special Topics: Big Data and ML Systems  Lakshminarayanan Subramanian
  Office Hours M 7:10-9:00PM CIWW 512
  

  Course Description:

  This class aims to cover introductory and recent concepts in big data and machine learning systems. The class will focus on a broad spectrum of big data computational problems, algorithms and platforms. The class will be centered around several big data case studies in commercial use to understand big data and machine learning tasks across different domains and provide an in-depth understanding of the problems and algorithms used in each domain. From the computational platform perspective, this class covers popular and widely used commercial platforms, Spark, streaming platforms, Tensorflow, ML platforms (MLBase, MxNet) and GPU platforms. The goal of this class is to educate students about the algorithms and systems techniques used to build scalable big data and ML platforms and how these platforms are used in the real world.

  This class covers the following broad topics:

  1. Cloud Infrastructure Blocks: AWS
  2. Big Data Algorithms: searching and indexing large data sets, implementing standard statistical algorithms, similar items, nearest neighbors, graph mining, network analytics, dimensionality reduction
  3. Big Data Computation Platforms: Spark, TensorFlow, MLBase, MLib, MxNet, GPU computing
  4. Scalable ML Algorithms: Implementing deep learning algorithms, graph and network analytics, scalable vision algorithms, scalable NLP algorithms
  5. Domain Specific Applications and Case Studies: vision analytics engines, NLP analytics engines, search and graph analytics
• CSCI-GA.3140-​001
  (20921) Abstract Interpretation  Patrick Cousot
  Office Hours M 5:10-7:00PM 60 Fifth Ave 446
  

  Course Description:

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

  
  
  Prerequisites: Some facility with logic, sets, linear algebra, graphs, formal languages, mathematical proofs, and programming languages is expected.

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