Courses tagged with "Nutrition" (421)

This course serves as an introduction to the basic principles that govern all aspects of our networked lives. We will learn about companies like Google and technologies like the Internet in a way that requires no mathematics beyond basic algebra.

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.

Acknowledgements

The course materials were developed jointly by Prof. Stephen Boyd (Stanford), who was a visiting professor at MIT when this course was taught, and Prof. Lieven Vanderberghe (UCLA).

This course is an introduction to copyright law and American law in general. Topics covered include: structure of federal law; basics of legal research; legal citations; how to use LexisNexis®; the 1976 Copyright Act; copyright as applied to music, computers, broadcasting, and education; fair use; Napster®, Grokster®, and Peer-to-Peer file-sharing; Library Access to Music Project; The 1998 Digital Millennium Copyright Act; DVDs and encryption; software licensing; the GNU® General Public License and free software.

This course is an introductory subject in the field of electric power systems and electrical to mechanical energy conversion. Electric power has become increasingly important as a way of transmitting and transforming energy in industrial, military and transportation uses. Electric power systems are also at the heart of alternative energy systems, including wind and solar electric, geothermal and small scale hydroelectric generation.

The course is designed to provide a practical - hands on - introduction to electronics with a focus on measurement and signals. The prerequisites are courses in differential equations, as well as electricity and magnetism. No prior experience with electronics is necessary. The course will integrate demonstrations and laboratory examples with lectures on the foundations. Throughout the course we will use modern "virtual instruments" as test-beds for understanding electronics. The aim of the course is to provide students with the practical knowledge necessary to work in a modern science or engineering setting.

This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time. Problem-based MATLAB assignments are given which require significant time on MATLAB.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Acknowledgements

The 6.094 course materials were developed by Danilo Šćepanović, Sourav R. Dey, Ankit Patel, and Patrick Ho.

Learn how to write composable software that stays responsive at all times by being elastic under load and resilient in the presence of failures. Model systems after human organizations or inter-human communication.

Introduction to the design and implementation of applications for handheld systems, such as smartphones and tablets, running the Android Platform.

In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.

This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of contexts from electrical engineering and computer science.

Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making.

Course Format

This course has been designed for independent study. It includes all of the materials you will need to understand the concepts covered in this subject. The materials in this course include:

• Lecture videos from Spring 2011, taught by Prof. Dennis Freeman
• Recitation videos, developed for OCW Scholar by teaching assistant Kendra Pugh
• Course notes
• Software and design labs
• Homework assignments and additional exercises
• Nano-quizzes and exams with solutions

Content Development

Traditionally, progress in electronics has been driven by miniaturization. But as electronic devices approach the molecular scale, classical models for device behavior must be abandoned. To prepare for the next generation of electronic devices, this class teaches the theory of current, voltage and resistance from atoms up. To describe electrons at the nanoscale, we will begin with an introduction to the principles of quantum mechanics, including quantization, the wave-particle duality, wavefunctions and Schrödinger's equation. Then we will consider the electronic properties of molecules, carbon nanotubes and crystals, including energy band formation and the origin of metals, insulators and semiconductors. Electron conduction will be taught beginning with ballistic transport and concluding with a derivation of Ohm's law. We will then compare ballistic to bulk MOSFETs. The class will conclude with a discussion of possible fundamental limits to computation.

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

本课程讨论人、媒介、信息在社会化媒体环境下的新规律。The course introduces students to regular patterns of interaction among people, media and information under our surrounding social media .

Use of available (mainly web-based) programs for analyzing biological data. This is Part 2 of an introductory course with a strong emphasis on hands-on methods. Some theory is introduced, but the main focus is on using extant bioinformatics tools to analyze data and generate biological hypotheses.

6.336J is an introduction to computational techniques for the simulation of a large variety of engineering and physical systems. Applications are drawn from aerospace, mechanical, electrical, chemical and biological engineering, and materials science. Topics include: mathematical formulations; network problems; sparse direct and iterative matrix solution techniques; Newton methods for nonlinear problems; discretization methods for ordinary, time-periodic and partial differential equations, fast methods for partial differential and integral equations, techniques for dynamical system model reduction and approaches for molecular dynamics.

This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5211 (Introduction to Numerical Simulation).

This course is an introduction to software engineering, using the Java™ programming language. It covers concepts useful to 6.005. Students will learn the fundamentals of Java. The focus is on developing high quality, working software that solves real problems.

The course is designed for students with some programming experience, but if you have none and are motivated you will do fine. Students who have taken 6.005 should not take this course. Each class is composed of one hour of lecture and one hour of assisted lab work.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

6.101 is an introductory experimental laboratory that explores the design, construction, and debugging of analog electronic circuits. Lectures and six laboratory projects investigate the performance characteristics of diodes, transistors, JFETs, and op-amps, including the construction of a small audio amplifier and preamplifier. Seven weeks are devoted to the design and implementation, and written and oral presentation of a project in an environment similar to that of engineering design teams in industry. The course provides opportunity to simulate real-world problems and solutions that involve trade offs and the use of engineering judgment. Engineers from local analog engineering companies come to campus to help students with their design projects.

6.111 is reputed to be one of the most demanding classes at MIT, exhausting many students' time and creativity. The course covers digital design topics such as digital logic, sequential building blocks, finite-state machines, FPGAs, timing and synchronization. The semester begins with lectures and problem sets, to introduce fundamental topics before students embark on lab assignments and ultimately, a digital design project. The students design and implement a final digital project of their choice, in areas such as games, music, digital filters, wireless communications, video, and graphics. The course relies on extensive use of Verilog® for describing and implementing digital logic designs on state-of-the-art FPGA.

This course will introduce you to the foundations of modern cryptography, with an eye toward practical applications.

Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization.