Online courses directory (273)

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.

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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 is an introduction to quantum computation, a cutting edge field that tries to exploit the exponential power of computers based on quantum mechanics. The course does not assume any prior background in quantum mechanics, and can be viewed as a very simple and conceptual introduction to that field.

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.