Courses tagged with "Nutrition" (212)

This freshman-level course is the second semester of introductory physics. The focus is on electricity and magnetism. The subject is taught using the TEAL (Technology Enabled Active Learning) format which utilizes small group interaction and current technology. The TEAL/Studio Project at MIT is a new approach to physics education designed to help students develop much better intuition about, and conceptual models of, physical phenomena.

Staff List

Acknowledgements

The TEAL project is supported by The Alex and Brit d'Arbeloff Fund for Excellence in MIT Education, MIT iCampus, the Davis Educational Foundation, the National Science Foundation, the Class of 1960 Endowment for Innovation in Education, the Class of 1951 Fund for Excellence in Education, the Class of 1955 Fund for Excellence in Teaching, and the Helena Foundation. Many people have contributed to the development of the course materials. (PDF)

Fundamental topics in electromagnetism: electric charge, electric fields, currents, magnetic fields, and induction.

Planet Earth, an overview of selected geological topics, discusses how earthquakes, volcanoes, minerals and rocks, energy, and plate tectonics have interacted over deep time to produce our dynamic island in space, and its unique resources.

This course describes the processes by which mass, momentum, and energy are transported in plasmas, with special reference to magnetic confinement fusion applications.

The Fokker-Planck collision operator and its limiting forms, as well as collisional relaxation and equilibrium, are considered in detail. Special applications include a Lorentz gas, Brownian motion, alpha particles, and runaway electrons.

The Braginskii formulation of classical collisional transport in general geometry based on the Fokker-Planck equation is presented.

Neoclassical transport in tokamaks, which is sensitive to the details of the magnetic geometry, is considered in the high (Pfirsch-Schluter), low (banana) and intermediate (plateau) regimes of collisionality.

This course introduces the design of feedback control systems as applied to a variety of air and spacecraft systems. Topics include the properties and advantages of feedback systems, time-domain and frequency-domain performance measures, stability and degree of stability, the Root locus method, Nyquist criterion, frequency-domain design, and state space methods.

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.

Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

An introduction to the principles of tomographic imaging and its applications. It includes a series of lectures with a parallel set of recitations that provide demonstrations of basic principles. Both ionizing and non-ionizing radiation are covered, including x-ray, PET, MRI, and ultrasound. Emphasis on the physics and engineering of image formation.

This course studies basic optimization and the principles of optimal control. It considers deterministic and stochastic problems for both discrete and continuous systems. The course covers solution methods including numerical search algorithms, model predictive control, dynamic programming, variational calculus, and approaches based on Pontryagin's maximum principle, and it includes many examples and applications of the theory.

The central theme of this course is the interaction of radiation with biological material. The course is intended to provide a broad understanding of how different types of radiation deposit energy, including the creation and behavior of secondary radiations; of how radiation affects cells and why the different types of radiation have very different biological effects. Topics will include: the effects of radiation on biological systems including DNA damage; in vitro cell survival models; and in vivo mammalian systems. The course covers radiation therapy, radiation syndromes in humans and carcinogenesis. Environmental radiation sources on earth and in space, and aspects of radiation protection are also discussed. Examples from the current literature will be used to supplement lecture material.

This course covers interpretations of the concept of probability. Topics include basic probability rules; random variables and distribution functions; functions of random variables; and applications to quality control and the reliability assessment of mechanical/electrical components, as well as simple structures and redundant systems. The course also considers elements of statistics; Bayesian methods in engineering; methods for reliability and risk assessment of complex systems (event-tree and fault-tree analysis, common-cause failures, human reliability models); uncertainty propagation in complex systems (Monte Carlo methods, Latin Hypercube Sampling); and an introduction to Markov models. Examples and applications are drawn from nuclear and other industries, waste repositories, and mechanical systems.

Quantum computation is a remarkable subject building on the great computational discovery that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course starts with a simple introduction to the fundamental principles of quantum mechanics using the concepts of qubits (or quantum bits) and quantum gates. This treatment emphasizes the paradoxical nature of the subject, including entanglement, non-local correlations, the no-cloning theorem and quantum teleportation. The course covers the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, Shor's quantum algorithm for factoring integers, as well as the prospects for quantum algorithms for NP-complete problems. It also discusses the basic ideas behind the experimental realization of quantum computers, including the prospects for adiabatic quantum optimization and the D-Wave controversy.

Before your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. Learn more.

Do I need a textbook for this class?
No. Notes will be posted each week. If you wish to consult other references, a list of related textbooks and online resources will be provided.

What is the estimated effort for course?
About 5-12 hrs/week.

Why is the work load range so wide?
How long you spend on the course depends upon your background and on the depth to which you wish to understand the material. The topics in this course are quite open ended, and will be presented so you can understand them at a high level or can try to follow it at a sophisticated level with the help of the posted notes.

How much does it cost to take the course?
Nothing! The course is free.

Will the text of the lectures be available?
Yes. All of our lectures will have transcripts synced to the videos.

Do I need to watch the lectures live?
No. You can watch the lectures at your leisure.

This subject introduces the key concepts and formalism of quantum mechanics and their relevance to topics in current research and to practical applications. Starting from the foundation of quantum mechanics and its applications in simple discrete systems, it develops the basic principles of interaction of electromagnetic radiation with matter.

Topics covered are composite systems and entanglement, open system dynamics and decoherence, quantum theory of radiation, time-dependent perturbation theory, scattering and cross sections. Examples are drawn from active research topics and applications, such as quantum information processing, coherent control of radiation-matter interactions, neutron interferometry and magnetic resonance.

The study of the night sky instilled wonder in our ancestors. Modern astronomy extends the human view to previously unexplored regions of space and time. In this course, you will gain an understanding of these discoveries through a focus on relativity—Einstein's fascinating and non-intuitive description of the physical world. By studying relativity and astronomy together, you will develop physical insight and quantitative skills, and you’ll regain a profound sense of wonder for the universe we call home.

FAQ

• What topics will the course cover?
  • Section One—Introduction
  • Section Two—3, 2, 1 … Launching the journey into spacetime
  • Section Three—Special relativity: from light to dark
  • Section Four—General relativity: from flat to curved

• Is there a required textbook?

  • No textbook is required. Notes will be posted weekly. A list of supplemental resources, including textbooks, will be provided.​​

• What are the learning outcomes of this course?

  • Explain the meaning and significance of the postulates of special and general relativity.

  • Discuss significant experimental tests of both special and general relativity.

  • Analyze paradoxes in special relativity.

  • Apply appropriate tools for problem solving in special relativity.

  • Describe astrophysical situations where the consequences of relativity qualitatively impact predictions and/or observations.

  • Describe daily situations where relativity makes a difference.

This course was created for the "product development" track of MIT's System Design and Management Program (SDM) in conjunction with the Center for Innovation in Product Development.  After taking this course, a student should be able to:

• Formulate measures of performance of a system or quality characteristics. These quality characteristics are to be made robust to noise affecting the system.
• Sythesize and select design concepts for robustness.
• Identify noise factors whose variation may affect the quality characteristics.
• Estimate the robustness of any given design (experimentally and analytically).
• Formulate and implement methods to reduce the effects of noise (parameter design, active control, adjustment).
• Select rational tolerances for a design.
• Explain the role of robust design techniques within the wider context of the product development process.
• Lead product development activities that include robust design techniques.