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Machine learning : an algorithmic perspective / Stephen Marsland.

By: Material type: TextTextSeries: Chapman & Hall/CRC machine learning & pattern recognition seriesPublication details: Boca Raton : CRC Press, c2015.Edition: 2nd edDescription: xx, 437 p. : illustrations ; 25 cmISBN:
  • 9781466583283 (hbk)
Subject(s): DDC classification:
  • 006.31 23 M372
Contents:
1. Introduction -- 2. Preliminaries -- 3. Neurons, neural networks, and linear discriminants -- 4. The multi-layer perceptron -- 5. Radial basis functions and splines -- 6. Dimensionality reduction -- 7. Probabilistic learning -- 8. Support vector machines -- 9. Optimisation and search -- 10. Evolutionary learning -- 11. Reinforcement learning -- 12. Learning with trees -- 13. Decision by committee: ensemble learning -- 14. Unsupervised learning -- 15. Markov chain Monte Carlo (MCMC) methods -- 16. Graphical models -- 17. Symmetric weights and deep belief networks -- 18. Gaussian processes -- Appendix A: Python-- Index.
Summary: This book helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 006.31 M372 (Browse shelf(Opens below)) Available 136298
Total holds: 0

Includes index.

1. Introduction --
2. Preliminaries --
3. Neurons, neural networks, and linear discriminants --
4. The multi-layer perceptron --
5. Radial basis functions and splines --
6. Dimensionality reduction --
7. Probabilistic learning --
8. Support vector machines --
9. Optimisation and search --
10. Evolutionary learning --
11. Reinforcement learning --
12. Learning with trees --
13. Decision by committee: ensemble learning --
14. Unsupervised learning --
15. Markov chain Monte Carlo (MCMC) methods --
16. Graphical models --
17. Symmetric weights and deep belief networks --
18. Gaussian processes --
Appendix A: Python--
Index.

This book helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

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