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  2. Andrew Ng - Wikipedia

    en.wikipedia.org/wiki/Andrew_Ng

    His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years. [24] [25] As of 2020, three of most popular courses on Coursera are Ng's: Machine Learning (#1), AI for Everyone (#5), Neural Networks and Deep Learning (#6). [26] In 2008, his group at Stanford was one of ...

  3. Daphne Koller - Wikipedia

    en.wikipedia.org/wiki/Daphne_Koller

    Daphne Koller (Hebrew: דפנה קולר; born August 27, 1968) is an Israeli-American computer scientist. She was a professor in the department of computer science at Stanford University [4] and a MacArthur Foundation fellowship recipient. [1]

  4. Chelsea Finn - Wikipedia

    en.wikipedia.org/wiki/Chelsea_Finn

    Chelsea Finn is an American computer scientist and assistant professor at Stanford University.Her research investigates intelligence through the interactions of robots, with the hope to create robotic systems that can learn how to learn.

  5. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s 'AI winter' caused by pessimism about machine learning effectiveness. 1980s: Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s: Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach.

  6. Udacity - Wikipedia

    en.wikipedia.org/wiki/Udacity

    Udacity is the outgrowth of free computer science classes offered in 2011 through Stanford University. [9] Thrun has stated he hopes half a million students will enroll, after an enrollment of 160,000 students in the predecessor course at Stanford, Introduction to Artificial Intelligence, [10] and 90,000 students had enrolled in the initial two classes as of March 2012.

  7. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering generic principles that allow a learning machine to be successful. For example, Bengio and LeCun (2007) wrote an article regarding local vs non-local learning, as well as shallow vs deep architecture. [231]

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