000			02584 a2200217 4500
005			20250416104308.0
008			240424b2023|||||||| |||| 00| 0 eng d
020			_a9780367433543
041			_aEnglish
082			_a006.31 K35
100			_aKalita, Jugal _eAuthor _93652
245			_aMachine learning: theory and practice
260			_bCRC Press, _c2023. _aBoca Raton:
300			_axv, 282p.; 23cms.
500			_aMachine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers. Table of Contents: 1. Introduction. 2. Regression. 3. Tree-Based Classi cation and Regression. 4. Arti cial Neural Networks. 5. Reinforcement Learning. 6. Unsupervised Learning. 7. Conclusions.
650			_aMachine learning _xUnsupervised learning _96243
650			_aRegression _xReinforcement learning _96244
650			_aTree-Based classification and regression _93654
650			_aArtificial neural networks _93655
942			_cBK
999			_c1180 _d1180