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The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. [8] [9] The synonym self-teaching computers was also used in this time period.
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]
The ideas of teaching machines and programmed learning provided the basis for later ideas such as open learning and computer-assisted instruction. Illustrations of early teaching machines can be found in the 1960 sourcebook, Teaching Machines and Programmed Learning. [12] An "Autotutor" was demonstrated at the 1964 World's Fair. [13]
The model was elaborated in more detail in their book Mind Over Machine (1986/1988). [2] A more recent articulation, "Revisiting the Six Stages of Skill Acquisition," authored by Stuart E. Dreyfus and B. Scot Rousse, appears in a volume exploring the relevance of the Skill Model: Teaching and Learning for Adult Skill Acquisition: Applying the ...
Adaptive learning, also known as adaptive teaching, is an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customized resources and learning activities to address the unique needs of each learner. [1]
Teacher forcing is an algorithm for training the weights of recurrent neural networks (RNNs). [1] It involves feeding observed sequence values (i.e. ground-truth samples) back into the RNN after each step, thus forcing the RNN to stay close to the ground-truth sequence.
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.
It used what would become core principles within the field of machine learning before they had been properly theorised. For example, the combination of how MENACE starts with equal numbers of types of beads in each matchbox, and how these are then selected at random, creates a learning behaviour similar to weight initialisation in modern ...