Jugal Kalita

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.

Theory and Practice

Machine Learning

The book has an introductory chapter that gets the reader started quickly with programming in Perl. The initial part of the book discusses Perl expressions, statements, control flow, built-in data types such as arrays and hashes, and complex data structures built using references. On Perl has several chapters covering specialized topics. The chapter on socket-based network programming deals with forking and using fork to write complex interactive client-server programs. There is a chapter with in-depth discussion of CGI programming including error-handling and security issues that arise. The chapter on web-client programming deals with writing programs that access Web pages, fill up GET and POST forms, handle cookies and redirected Web pages. The book has several unique chapters not found in any other book on Perl in the market. The chapter on security discusses hashes such as MD5, message authentication codes (MACs), digital signature schemes, and encryption techniques such as DES, Rijndael, and RSA. Other chapters deal with writing recursive programs that work with files and directories; this chapter also discusses predefined modules that deal with portability in file names and paths across operating systems, recursive traversal of file hierarchies and tarring and untarring of filles. The chapter on functional programming illustrates that Perl functions are first-class, can be used to write closures and can be composed to form more complex functions. In particular, this can be useful for programming in artificial intelligence.

Perl for Students and Professionals

On Perl

Valentina Emilia Balas

Samarjeet Borah

Ratika Pradhan

This book presents high-quality papers from an international forum for research on computational approaches to learning. It includes current research and findings from various research labs, universities and institutions that may lead to development of marketable products. It also provides solid support for these findings in the form of empirical studies, theoretical analysis, or comparison to psychological phenomena. Further, it features work that shows how to apply learning methods to solve important application problems as well as how machine learning research is conducted. The book is divided into two main parts: Machine Learning Techniques, which covers machine learning-related research and findings; and, Data Analytics, which introduces recent developments in this domain. Additionally, the book includes work on data analytics using machine learning techniques.

Jugal Kalita

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