About the Book:
Machine
Learning: An Algorithmic Perspective, Second Edition 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.
New to the
Second Edition - Two new chapters on deep belief
networks and Gaussian processes
- Reorganization of the chapters to
make a more natural flow of content
- Revision of the support vector
machine material, including a simple implementation for experiments
- New material on random forests,
the perceptron convergence theorem, accuracy methods, and conjugate
gradient optimization for the multi-layer perceptron
- Additional discussions of the
Kalman and particle filters
- Improved code, including better
use of naming conventions in Python
- The text
strongly encourages students to practice with the code. Each chapter includes
detailed examples along with further reading and problems. All of the code used
to create the examples is available on the author’s website.
Features · Reflects
recent developments in machine learning, including the rise of deep belief
networks ·
Presents
the necessary preliminaries, including basic probability and statistics ·
Discusses
supervised learning using neural networks ·
Covers
dimensionality reduction, the EM algorithm, nearest neighbor methods, optimal
decision boundaries, kernel methods, and optimization ·
Describes
evolutionary learning, reinforcement learning, tree-based learners, and methods
to combine the predictions of many learners ·
Examines
the importance of unsupervised learning, with a focus on the self-organizing
feature map · Explores modern, statistically based approaches to
machine learning |
