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Bibliography
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- Machine Learning
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- 27 May 2022
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- 31 March 2022, pp 327-334
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3 - Basic Parametric Models and a Statistical Perspective on Learning
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- Machine Learning
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- 27 May 2022
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- 31 March 2022, pp 37-62
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1 - Introduction
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- Machine Learning
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- 31 March 2022, pp 1-12
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6 - Neural Networks and Deep Learning
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- 31 March 2022, pp 133-162
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Acknowledgements
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- Machine Learning
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- 31 March 2022, pp ix-x
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2 - Supervised Learning: A First Approach
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- Machine Learning
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- 31 March 2022, pp 13-36
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Frontmatter
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- Machine Learning
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- 31 March 2022, pp i-iv
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11 - User Aspects of Machine Learning
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- Machine Learning
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- 31 March 2022, pp 287-308
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Notation
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Contents
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- Machine Learning
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- 31 March 2022, pp v-viii
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4 - Understanding, Evaluating, and Improving Performance
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- Machine Learning
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- 31 March 2022, pp 63-90
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5 - Learning Parametric Models
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- Machine Learning
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- 31 March 2022, pp 91-132
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8 - Non-linear Input Transformations and Kernels
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- 31 March 2022, pp 189-216
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![](https://assets.cambridge.org/97811088/43607/cover/9781108843607.jpg)
Machine Learning
- A First Course for Engineers and Scientists
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- 27 May 2022
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- 31 March 2022
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10 - Generative Models and Learning from Unlabelled Data
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- Machine Learning
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- 31 March 2022, pp 247-286
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Index
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- 31 March 2022, pp 335-338
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9 - The Bayesian Approach and Gaussian Processes
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- 31 March 2022, pp 217-246
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12 - Ethics in Machine Learning
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- Machine Learning
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- 31 March 2022, pp 309-326
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7 - Ensemble Methods: Bagging and Boosting
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- 31 March 2022, pp 163-188
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Contesting Marginality: The Boreal Forest of Middle Scandinavia and the Worlds Outside
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- The Global North
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- Amsterdam University Press
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- 20 January 2022
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- 31 December 2021, pp 9-36
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