The intelligent student does not desire what is commonly called a ``Key'' to a collection of problems. He welcomes, at the most, some hints as to the method by which a problem is to be solved, or some indication of the principal steps in the process.

I have tried to illustrate as many principles and methods as possible; and therefore when a group of questions might have been solved by processes identical in principle, I have preferred to treat different questions of the group by different methods.

The present work is an outcome of this pursuit, which will serve as a complete guide to private students reading the subject with few or no opportunities of instruction. This will save the time and lighten the work of Teachers as well. This book helps in acquiring a better understanding of the basic principles of machine learning algorithms related to pattern recognition and in revising a large amount of the subject matter quickly.

This book provides conceptual solutions to all the exercises of the textbook Pattern Recognition and Machine Learning (Christopher M. Bishop). This leads to simplification of algorithmic paradigm and enforces correctness as a part of algorithm design with great emphasis on foundational science of algorithms. Familiarity with elementary calculus and probability is assumed. Though aimed primarily at programmers, it imparts the knowledge of deep internals of underlying mathematical concepts to teachers alike.

Care has been taken, as in the forthcoming ones, to present the solutions with multi-concepts and beyond in a simple natural manner, in order to meet the difficulties which are most likely to arise, and to render the work intelligible and instructive.

This work contains several variations of problems, solutions, methods, approaches to enrich, strengthen and enliven the inherent multi-concepts.