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  2. CS50 - Wikipedia

    en.wikipedia.org/wiki/CS50

    CS50 (Computer Science 50) [a] is an introductory course on computer science taught at Harvard University by David J. Malan. The on-campus version of the course is Harvard's largest class with 800 students, 102 staff, and up to 2,200 participants in their regular hackathons .

  3. David J. Malan - Wikipedia

    en.wikipedia.org/wiki/David_J._Malan

    David Jay Malan (/ m eɪ l ɛ n /) is an American computer scientist and professor. Malan is Gordon McKay Professor of Computer Science at Harvard University, and is best known for teaching the course CS50, [2] [3] which is the largest open-learning course at Harvard University and Yale University and the largest massive open online course at EdX, with lectures being viewed by over a million ...

  4. List of programming languages for artificial intelligence

    en.wikipedia.org/wiki/List_of_programming...

    C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform. Smalltalk has been used extensively for simulations, neural networks, machine learning, and genetic algorithms.

  5. 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. [ 22 ] [ 23 ] 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).

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

  7. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...

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