Languages and compilers to exploit multithreaded parallelism. Implicit parallel programming using functional languages and their extensions. Higher-order functions, non-strictness, and polymorphism. Explicit parallel programming and nondeterminism. The lambda calculus and its variants. Term rewriting and operational semantics. Compiling multithreaded code for symmetric multiprocessors and clusters. Static analysis and compiler optimizations.

Computer-aided design methodologies for synthesis of multivariable feedback control systems. Performance and robustness trade-offs. Model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; nonlinear effects. Computer-aided (MATLAB) design homework using models of physical processes. This course uses computer-aided design methodologies for synthesis of multivariable feedback control systems. Topics covered include: performance and robustness trade-offs; model-based compensators; Q-parameterization; ill-posed optimization problems; dynamic augmentation; linear-quadratic optimization of controllers; H-infinity controller design; Mu-synthesis; model and compensator simplification; and nonlinear effects. The assignments for the course comprise of computer-aided (MATLABĺ¨) design problems.

Relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of the analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; augmented transition network grammars. Question answering systems. Extensive laboratory work on building natural language processing systems. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Today, computer-based simulations are becoming increasingly popular, especially when daylighting and energy conservation are amongst the key goals for a project. This two-week workshop will expose participants to the current daylighting simulation models and beyond, by introducing realistic and dynamic assessment methods through hands-on exercises and application to a design project. Open to students and practitioners.

This course is a graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, and finance, as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. We will also consider other extensions of network flow problems.

The course draws on faculty members from the Center for Real Estate, the City Design and Development Group (Department of Urban Studies and Planning), and the Media Lab to explore extraordinary projects that challenge conventional approaches to real estate development, urban design, and advanced digital technology.

The Non-Programmers' Tutorial For Python is a tutorial designed to be an introduction to the Python programming language. This guide is for someone with no programming experience.

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB programming.

Complément d’exercices théoriques et pratiques en complement du livre Algorithms, 4th Edition de Robert Sedgewick and Kevin Wayne pour une classe inversée.

The Ocean Health Index is a new, comprehensive measure of the ocean’s overall condition – one that treats people and nature as integrated parts of a healthy system. The ocean touches nearly every aspect of our lives – making it essential to the economic, social, and ecological well-being of everyone, everywhere. Evaluated globally and by country, the Ocean Health Index presents 10 public goals that represent the wide range of benefits that a healthy ocean provides to people. Each country’s overall score is the average of its 10 goal scores. Overall scores and individual goal scores are directly comparable between all countries. All scores range from 0 to 100.

This course makes students familiar with the design of offshore wind farms in general and focuses on the foundation design in particular. The course is based on actual cases of real offshore wind farms that have been built recently or will be built in the near future.

The goal of this project is to free undergraduate computer science students from having to pay for an introductory data structures book. I have decided to implement this goal by treating this book like an Open Source software project. The LATEX source, Java source, and build scripts for the book are available to download from the author’s website and also, more importantly, on a reliable source code management site.

This course teaches the fundamentals of engineering operating systems. The following topics are studied in detail: virtual memory, kernel and user mode, system calls, threads, context switches, interrupts, interprocess communication, coordination of concurrent activities, and the interface between software and hardware. Most importantly, the interactions between these concepts are examined. The course is divided into two blocks; the first block introduces one operating system, UNIXĺ¨ v6, in detail. The second block of lectures covers important operating systems concepts invented after UNIXĺ¨ v6, which was introduced in 1976.

The course presents an overview of the history and structure of modern operating systems, analyzing in detail each of the major components of an operating system, and exploring more advanced topics in the field, such as security concerns. Upon successful completion of this course, the student will be able to: explain what an operating system does and how it is used; identify the various components of a computer system and how they interact with an operating system; describe the differences between a 32-bit and 64-bit operating system; explain the different types of operating systems and the major ones in use today; discuss the importance and use of threads and processes in an operating system; describe concurrency; explain the difference between a thread and a process; discuss context switching and how it is used in an operating system; describe synchronization; explain a race condition; discuss interprocess communication; describe how semaphores can be used in an operating system; discuss three of the classic synchronization problems; explain the alternatives to semaphores; discuss CPU scheduling and its relevance to operating systems; explain the general goals of CPU scheduling; describe the differences between pre-emptive and non-preemptive scheduling; discuss four CPU scheduling algorithms; explain what deadlock is in relation to operating systems; discuss deadlock prevention, avoidance, and their differences; describe deadlock detection and recovery; explain the memory hierarchy; discuss how the operating system interacts with memory; describe how virtual memory works; discuss three algorithms for dynamic memory allocation; explain methods of memory access; describe paging and page replacement algorithms; describe a file system and its purpose; discuss various file allocation methods; explain disk allocation and associated algorithms; discuss types of security threats; describe the various types of malware; explain basic security techniques; explain basic networking principles; discuss protocols and how they are used; explain reference models, particularly TCP/IP and OSI. (Computer Science 401)

In this book, you will learn about all three kinds of interaction. In all three cases, interesting software techniques are needed in order to bring the computations into contact, yet keep them sufifciently at arm’s length that they don’t compromise each other’s reliability. The exciting challenge, then, is supporting controlled interaction. This includes support for computations that share a single computer and interact with one another, as your email and word processing programs do. It also includes support for data storage and network communication. This book describes how all these kinds of support are provided both by operating systems and by additional software layered on top of operating systems, which is known as middleware.

This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods.

Increasingly, we are realizing that to make computer systems more intelligent and responsive to users, we will have to make them more sensitive to context. Traditional hardware and software design overlooks context because it conceptualizes systems as input-output functions. Systems take input explicitly given to them by a human, act upon that input alone and produce explicit output. But this view is too restrictive. Smart computers, intelligent agent software, and digital devices of the future will also have to operate on data that they observe or gather for themselves. They may have to sense their environment, decide which aspects of a situation are really important, and infer the user's intention from concrete actions. The system's actions may be dependent on time, place, or the history of interaction. In other words, dependent upon context. But what exactly is context? We'll look at perspectives from machine learning, sensors and embedded devices, information visualization, philosophy and psychology. We'll see how each treats the problem of context, and discuss the implications for design of context-sensitive hardware and software. Course requirements will consist of critiques of class readings [about 3 papers/week], and a final project [paper or computer implementation project].

This is an advanced interdisciplinary introduction to applied parallel computing on modern supercomputers. It has a hands-on emphasis on understanding the realities and myths of what is possible on the world's fastest machines. We will make prominent use of the Julia Language software project.

"This course introduces fundamentals of shared and distributed memory programming, teaches you how to code using openMP and MPI respectively, and provides hands-on experience of parallel computing geared towards numerical applications."

Here is the link to the new Passion-Driven Statistics e-book!

http://bit.ly/Passion-DrivenStatistics

Passion-Driven Statistics is an NSF-funded, multidisciplinary, project-based curriculum that supports students in conducting data-driven research, asking original questions, and communicating methods and results using the language of statistics. The curriculum supports students to work with existing data covering psychology, health, earth science, government, business, education, biology, ecology and more. From existing data, students are able to pose questions of personal interest and then use statistical software (e.g. SAS, R, Python, Stata, SPSS) to answer them. The e-book is presented in pdf format for ease of use across platforms. It can also be customized by downloading and editing the .iba file (available through the link below) using the free “iBook Author” software.

http://bit.ly/EditPDSe-book

For more information, contact Lisa Dierker, ldierker@wesleyan.edu or check out the Passion-Driven Statistics website at https://passiondrivenstatistics.com/