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By September 2004, 900 MIT courses were available online. In 2005, MIT OpenCourseWare and other open educational resources projects formed the OpenCourseWare Consortium, which seeks to extend the reach and impact of open course materials, foster new open course materials and develop sustainable models for open course material publication.
There is more than 200 courses available on Maktabkhooneh for free. The "ocw.um.ac.ir (Persian: سامانه فیلم های آموزشی دانشگاه فردوسی مشهد)" is an online educational platform in Iran which provides free online courses from Ferdowsi University of Mashhad in Iran. The motto of the ocw.um.ac.ir is "Making ...
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning ...
Coursera Inc. (/ k ər ˈ s ɛ r ə /) is an American global massive open online course provider. It was founded in 2012 [2] [3] by Stanford University computer science professors Andrew Ng and Daphne Koller. [4] Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects.
The competitive ratio of an online problem is the best competitive ratio achieved by an online algorithm. Intuitively, the competitive ratio of an algorithm gives a measure on the quality of solutions produced by this algorithm, while the competitive ratio of a problem shows the importance of knowing the future for this problem.
The program schedules pairs based on spaced repetition algorithms. Without a computer program, the user has to schedule physical flashcards; this is time-intensive and limits users to simple algorithms like the Leitner system. [34] To optimize review schedules, developments in spaced repetition algorithms focus on predictive modeling.
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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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