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  2. Artificial intelligence in hiring - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence_in...

    Artificial intelligence can be used to automate aspects of the job recruitment process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants.

  3. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.

  4. Feature engineering - Wikipedia

    en.wikipedia.org/wiki/Feature_engineering

    Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods like Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear ...

  5. Moravec's paradox - Wikipedia

    en.wikipedia.org/wiki/Moravec's_paradox

    The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. The mental abilities of a four-year-old that we take for granted – recognizing a face, lifting a pencil, walking across a room, answering a question – in fact solve some of the hardest engineering problems ever conceived...

  6. Active learning (machine learning) - Wikipedia

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

    Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...

  7. Multi-task learning - Wikipedia

    en.wikipedia.org/wiki/Multi-task_learning

    Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.

  8. Manifold regularization - Wikipedia

    en.wikipedia.org/wiki/Manifold_regularization

    In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space.

  9. Sample complexity - Wikipedia

    en.wikipedia.org/wiki/Sample_complexity

    In addition to the supervised learning setting, sample complexity is relevant to semi-supervised learning problems including active learning, [7] where the algorithm can ask for labels to specifically chosen inputs in order to reduce the cost of obtaining many labels.