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This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.
The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.
Related Link(s)
Sample Chapter(s)
Preface
Chapter 1: Introduction
Contents:
- Preface
- Introduction
- Probability and Information
- Linear Algebra and Optimization
- Linear and Nonlinear Regression
- Perceptron
- Multilayer Perceptron
- Learning Theory
- Model Selection
- Clustering
- Radial Basis Networks
- Support Vector Machines
- Deep Learning
- Convolutional Networks
- Recurrent Networks
- Autoencoders
- Epilogue
- Bibliography
- Index
Readership: Professionals, academics, researchers, and graduate students in artificial intelligence/machine learning, neural networks, pattern recognition, and machine perception/computer vision.
