Machine 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.
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