000			01368nam a22002417a 4500
999			_c56474 _d56474
005			20200306113434.0
008			200306b ||||| |||| 00| 0 eng d
020			_a9781259096952
028			_aBrijwasi Book Distributors _bH-87, Lalita Park,Laxmi Nagar,Delhi-110092 _cBW/005744 _d28/02/2020
082			_a006.31 MIT
100			_aTom M Mitchell
245			_aMachine Learning _c/Tom M Mitchell
260			_aNew York _b: McGraw-Hill _c, ©1997.
300			_axvii, 414 pages _c; 25 cm
365			_b615
490			_aMcGraw-Hill series in computer science.
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			_a Mitchell 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			_aComputer Science
655			_aMachine learning _bComputer algorithms
700			_aTom M Mitchell
942			_cBOOK