Courses tagged with "Nutrition" (421)

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.

Learn about traditional and mobile malware, the security threats they represent, state-of-the-art analysis and detection techniques, and the underground ecosystem that drives such a profitable but illegal business.

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

This subject offers an interactive introduction to discrete mathematics oriented toward computer science and engineering. The subject coverage divides roughly into thirds:

1. Fundamental concepts of mathematics: Definitions, proofs, sets, functions, relations.
2. Discrete structures: graphs, state machines, modular arithmetic, counting.
3. Discrete probability theory.

On completion of 6.042J, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.

Interactive site components can be found on the Unit pages in the left-hand navigational bar, starting with Unit 1: Proofs.

This course introduces the fundamentals of machine tool and computer tool use. Students work with a variety of machine tools including the bandsaw, milling machine, and lathe. Instruction given on MATLAB®, MAPLE®, XESS™, and CAD. Emphasis is on problem solving, not programming or algorithmic development. Assignments are project-oriented relating to mechanical engineering topics. It is recommended that students take this subject in the first IAP after declaring the major in Mechanical Engineering.

This course was co-created by Prof. Douglas Hart and Dr. Kevin Otto.

Metadata is an unsung hero of the modern world, the plumbing that makes the information age possible. This course describes how Metadata is used as an information tool for the Web, for databases, and for the software and computing applications around us.

This course introduces the theory and technology of micro/nano fabrication. Lectures and laboratory sessions focus on basic processing techniques such as diffusion, oxidation, photolithography, chemical vapor deposition, and more. Through team lab assignments, students are expected to gain an understanding of these processing techniques, and how they are applied in concert to device fabrication. Students enrolled in this course have a unique opportunity to fashion and test micro/nano-devices, using modern techniques and technology.

6.012 is the header course for the department's "Devices, Circuits and Systems" concentration. The topics covered include modeling of microelectronic devices, basic microelectronic circuit analysis and design, physical electronics of semiconductor junction and MOS devices, relation of electrical behavior to internal physical processes, development of circuit models, and understanding the uses and limitations of various models. The course uses incremental and large-signal techniques to analyze and design bipolar and field effect transistor circuits, with examples chosen from digital circuits, single-ended and differential linear amplifiers, and other integrated circuits.

How do you design a mobile app that truly changes people's lives? How can you understand how a new service is being used, both quantitatively and qualitatively? How can you use all of the rich sensing and I/O capabilities of mobile devices to create experiences that go far beyond what's possible on a traditional computer?

Mobile devices are changing the ways that we interact with each other and information in the world. This course will take you from a domain of interest, through generative research, design, usability, implementation and field evaluation of a novel mobile experience. You'll finish the course with a working, field-tested application suitable for release in the app store as well as a deep understanding of human interaction with mobile devices and services.

Based on a popular MIT class that has been taught since 2006 by Frank Bentley of Yahoo Labs and Ed Barrett, a Senior Lecturer at MIT, this course will explore what makes mobile devices unique. A primary focus will be on studying existing behavior and using key findings for design. While writing the code for an app is a part of the class, the majority of the topics will cover designing and evaluating a unique mobile experience. Along the way, you will have opportunities to share your work with other students from around the world! Java experience (or Objective C for iOS users) and a smartphone are required.

All required readings are available within the courseware, courtesy of The MIT Press. A print version of the course textbook, Building Mobile Experiences, is also available for purchase. The MIT Press is offering enrolled students a special 30% discount on books ordered directly through the publisher’s website. To take advantage of this offer, please use promotion code BME30 at The MIT Press site.

MASLab (Mobile Autonomous System Laboratory), also known as 6.186, is a robotics contest. The contest takes place during MIT's Independent Activities Period and participants earn 6 units of P/F credit and 6 Engineering Design Points. Teams of three to four students have less than a month to build and program sophisticated robots which must explore an unknown playing field and perform a series of tasks.

MASLab provides a significantly more difficult robotics problem than many other university-level robotics contests. Although students know the general size, shape, and color of the floors and walls, the students do not know the exact layout of the playing field. In addition, MASLab robots are completely autonomous, or in other words, the robots operate, calculate, and plan without human intervention. Finally, MASLab is one of the few robotics contests in the country to use a vision based robotics problem.

So you’ve heard mobile is kind of a big deal, and you’re not sure how to transform your traditional desktop-focused web apps into fast, effective mobile experiences. This course is designed to teach web developers what they need to know to create great cross-device mobile web experiences. This course will focus on building mobile web apps, which will work across multiple platforms including Android, iOS, and others.

6.161 offers an introduction to laboratory optics, optical principles, and optical devices and systems. This course covers a wide range of topics, including: polarization properties of light, reflection and refraction, coherence and interference, Fraunhofer and Fresnel diffraction, holography, imaging and transforming properties of lenses, spatial filtering, two-lens coherent optical processor, optical properties of materials, lasers, electro-optic, acousto-optic and liquid-crystal light modulators, optical detectors, optical waveguides and fiber-optic communication systems. Students engage in extensive oral and written communication exercises. There are 12 engineering design points associated with this subject.

The topics covered in this course include:

• 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

This course is worth 4 Engineering Design Points.

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 (MATLAB®) design problems.

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.

In this class, you will learn fundamental algorithms and mathematical models for processing natural language, and how these can be used to solve practical problems.

Have you ever wondered how to build a system that automatically translates between languages? Or a system that can understand natural language instructions from a human? This class will cover the fundamentals of mathematical and computational models of language, and the application of these models to key problems in natural language processing.