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.

We intend this book to act as a guide writ large for would-be champions of OER, that anyone—called to action by the example set by our chapter authors—might serve as guides themselves. The following chapters tap into the deep experience of practitioners who represent a meaningful cross section of higher education institutions in North America. It is our hope that the examples and discussions presented by our authors will facilitate connections among practitioners, foster the development of best practices for OER adoption and creation, and more importantly, lay a foundation for novel, educational excellence.

Your action plan is an internal planning document for how you will convince key internal and external constituents to support for the work that you are doing. It is intended as a living document that you can revisit as you review the results of your advocacy activities and refine your advocacy strategy. Think of it as a skeleton you can work to fill in.

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.

The course purpose is to provide the substance and skill necessary to make sound business decisions relating to information systems, and to work with senior line managers in the resolution of issues and problems in this area. Categories of issues which will be addressed in the course include: How do IT and its various manifestations in business, such as the Internet, affect current and future competitiveness? How do we align business strategy and plans with IT strategy and IT plans? How can we engage executives in learning and leading IT-related change? How do we implement new systems, change work behavior, manage projects? How should we organize and govern IT in an organization.

This course provides a thorough introduction to the C programming language, the workhorse of the UNIX operating system and lingua franca of embedded processors and micro-controllers. The first two weeks will cover basic syntax and grammar, and expose students to practical programming techniques. The remaining lectures will focus on more advanced concepts, such as dynamic memory allocation, concurrency and synchronization, UNIX signals and process control, library development and usage. Daily programming Assignments and Labs and weekly laboratory exercises are required. Knowledge of C is highly marketable for summer internships, UROPs, and full-time positions in software and embedded systems development.

Principles of Computer System Design: An Introduction is published in two parts. Part I, containing chapters 1-6, is a traditional printed textbook published by Morgan Kaufman, an imprint of Elsevier. Part II, containing chapters 7-11, is available here as an open educational resource. This textbook, an introduction to the principles and abstractions used in the design of computer systems, is an outgrowth of notes written for 6.033 Computer System Engineering over a period of 40-plus years. Individual chapters are also used in other EECS subjects. There is also a Web site for the current 6.033 class with a lecture schedule that includes daily Assignments and Labs, lecture notes, and lecture slides. The 6.033 class Web site also contains a thirteen-year archive of class Assignments and Labs, design projects, and quizzes. Technical Requirements: Special software is required to use some of the files in this resource: .key, .ppt, and .zip.

Welcome to 6.041/6.431, a subject on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. For example: The concept of statistical significance (to be touched upon at the end of this course) is considered by the Financial Times as one of "The Ten Things Everyone Should Know About Science". A recent Scientific American article argues that statistical literacy is crucial in making health-related decisions. Finally, an article in the New York Times identifies statistical data analysis as an upcoming profession, valuable everywhere, from Google and Netflix to the Office of Management and Budget. The aim of this class is to introduce the relevant models, skills, and tools, by combining mathematics with conceptual understanding and intuition.

This course is an introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs, advice, and interactive proof systems in the quantum world. The objective is to bring students to the research frontier.

This course examines quantum computation and quantum information. Topics include quantum circuits, quantum Fourier transform and search algorithms, physical implementations, the quantum operations formalism, quantum error correction, stabilizer and Calderbank-Shor-Steans codes, fault tolerant quantum computation, quantum data compression, entanglement, and proof of the security of quantum cryptography. Prior knowledge of quantum mechanics and basic information theory is required.

Advanced seminar extends computer and analytic skills developed in the other subjects in this sequence into a research environment. Students present a structured discussion of a journal article representative of current research in Planning Decision Support Systems, and complete an approved short research project. Suggested research projects include topics related to ongoing research projects of the Computer Resource Laboratory. Seminar participants and invited guests will lead critical discussions of current literature and ongoing research. Each student will be responsible for identifying, reviewing, and presenting one structured discussion of articles from the current literature that are relevant to their research topic. The remaining time will be spent working on individual projects or thesis proposals. This fall, the seminar will focus on the following core issues that underlie most implementations of urban information systems and decision support tools: the sustainable acquisition and representation of urban knowledge; the emergent technological infrastructure for supporting metropolitan decision-making; and the innovative organizational and institutional arrangements that can take advantage of modern urban information systems.

Upon completion of this course, students will be able to: develop and research a topic of global significance; recognize authors‰ŰŞ arguments and the political, social and economic motivations behind their work; demonstrate the ability to locate, interpret and cite the relevant and appropriate information resources on a topic; and, demonstrate an understanding of the information research process.Login: guest_oclPassword: ocl

The Software Tools for Academics and Researchers (STAR) program at MIT seeks to bridge the divide between scientific research and the classroom. Understanding and applying research methods in the classroom setting can be challenging due to time constraints and the need for advanced equipment and facilities. The multidisciplinary STAR team collaborates with faculty from MIT and other educational institutions to design software exploring core scientific research concepts. The goal of STAR is to develop innovative and intuitive teaching tools for classroom use. All of the STAR educational tools are freely available. To complement the educational software, the STAR website contains curriculum components/modules which can facilitate the use of STAR educational tools in a variety of educational settings. Students, teachers, and professors should feel welcome to download software and curriculum modules for their own use. Online Publication

This course covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.

The smart grid of the future is a complex electrical power system. Its study, design, and management requires the integration of knowledge from various disciplines including sustainability, technology and mathematics.

Smart grids show a level of complexity and heterogeneity that often cannot be covered by analytical methods. Therefore, modeling and simulation are of great importance.

In this course, you will apply modeling tools to study and analyze the performance of your self-designed intelligent electrical power grid. By modeling smart grids, you will explore the integration of renewable energy sources into a grid, its dynamics, control and cyber security.

6.301 is a course in analog circuit analysis and design. We cover the tools and methods necessary for the creative design of useful circuits using active devices. The class stresses insight and intuition, applied to the design of transistor circuits and the estimation of their performance. We concentrate on circuits using the bipolar junction transistor, but the techniques that we study can be equally applied to circuits using JFETs, MOSFETs, MESFETs, future exotic devices, or even vacuum tubes.

Extends the computing and geographic information systems (GIS) skills developed in 11.520 to include spatial data management in client/server environments and advanced GIS techniques. First half covers the content of 11.523, introducing database management concepts, SQL (Structured Query Language), and enterprise-class database management software. Second half explores advanced features and the customization features of GIS software that perform analyses for decision support that go beyond basic thematic mapping. Includes the half-semester GIS project of 11.524 that studies a real-world planning issue.

Technical Project Management in Living and Geometric Order demonstrates that even the best-laid project plans can be undone by new technologies, financial upheavals, or resource scarcity, to name just a few disruptors. It encourages project managers to focus on learning throughout a project, with the understanding that what they learn could necessitate major changes in midstream. This adaptive, flexible, living-order approach is inspired by Lean in construction projects and Agile in software development. Technical Project Management in Living and Geometric Order explains how today’s projects unfold in dynamic environments in response to unexpected events. With its practical tips, detailed graphics, links to additional resources, and interviews with engineering professionals, it’s an accessible introduction to the living order for aspiring project managers.

This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system.