000			01464nam a22002897a 4500
005			20220512160615.0
008			220512b |||||||| |||| 00| 0 eng d
020			_a9780071154673
028			_a:Donated from Prof Madan Gopal
082			_a006.31 MIT
100			_aMitchell, Tom M
245			_aMachine Learning _c/ Tom M Mitchell
260			_aNew York _b : McGraw-Hill, _c©1997.
300			_axvii, 414 pages _b: illustrations _c; 25 cm
365			_bDonated
505			_a 1. Introduction -- 2. Concept Learning and the General-to-Specific Ordering -- 3. Decision Tree Learning -- 4. Artificial Neural Networks -- 5. Evaluating Hypotheses -- 6. Bayesian Learning -- 7. Computational Learning Theory -- 8. Instance-Based Learning -- 9. Genetic Algorithms -- 10. Learning Sets of Rules -- 11. Analytical Learning -- 12. Combining Inductive and Analytical Learning -- 13. Reinforcement Learning.
520			_aMitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data
650			_a Computer Science
650			_a Special computer methods (e.g. AI, multimedia, VR)[4]
650			_aMachine learning.
650			_aComputer algorithms.
650			_aArtificial intelligence.
650			_aMachine-learning.
650			_aAlgorithmes.
700			_aMitchell, Tom M.
942			_cMDL
999			_c59216 _d59216