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  2. Multi-task learning - Wikipedia

    en.wikipedia.org/wiki/Multi-task_learning

    In many applications, joint learning of unrelated tasks which use the same input data can be beneficial. The reason is that prior knowledge about task relatedness can lead to sparser and more informative representations for each task grouping, essentially by screening out idiosyncrasies of the data distribution.

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

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

    A structured general-purpose dataset on life, work, and death of 1.22 million distinguished people. Public domain. A five-step method to infer birth and death years, gender, and occupation from community-submitted data to all language versions of the Wikipedia project.

  4. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning also has intimate ties to optimization: Many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a ...

  5. Applications of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Applications_of_artificial...

    The use of AI in applications such as online trading and decision-making has changed major economic theories. [66] For example, AI-based buying and selling platforms estimate personalized demand and supply curves, thus enabling individualized pricing. AI systems reduce information asymmetry in the market and thus make markets more efficient. [67]

  6. Automated machine learning - Wikipedia

    en.wikipedia.org/wiki/Automated_machine_learning

    Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.

  7. Learnable function class - Wikipedia

    en.wikipedia.org/wiki/Learnable_function_class

    It is worth noting that at least for supervised classification and regression problems, if a function class is learnable, then the empirical risk minimization automatically satisfies . [2] Thus in these settings not only do we know that the problem posed by ( 1 ) is solvable, we also immediately have an algorithm that gives the solution.

  8. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]

  9. Machine learning control - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_control

    One example is the computation of sensor feedback from a known full state feedback. Neural networks are commonly used for such tasks. [4] Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the cost function of the plant. In this case, neither a model, the control law ...