Machine learning is a rapidly changing field within artificial intelligence, as more algorithms are identified and a theory of which algorithm will suit which purpose emerges. Artificial Learning provides a comprehensive introduction to all aspects of the subject and will be both aninvaluable text for students and a reference for practitioners seeking an up-to-date review.
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Some basic ideas
Learning algorithms with numeric input
Association and neural networks
6 other sections not shown
action applied attributes basic predicates Boltzmann machine branching ratio called choose chunking classification clause column concepts conceptual clustering conflict set consists contains context decision tree depends described Dido disjunctive eight queens problem empty entropy estimate example experiment facts father_of Jane finite function FunG genetic algorithms goal grammar graph Hamming distance heuristic hierarchy ideal trace input invent language layer leaf learner learning algorithms lemma machine macros matches method metric naive neural node notion occur operators operators monotonic output pair parse tree path pattern perceptron position possible practical conditional PredG probability problem solver program synthesis Prolog properties RAM chips real numbers recursive rule search space searcher sequence simple solution solve sort stack strings structure subgoal subset subterm Suppose symbol task theorem theory training instance training set true variables weights