This two-volume (1880-6) teaching aid for the Cambridge Mathematical Tripos greatly influenced the education of Srinivasa Ramanujan (1887-1920).

This book has been considered by academicians and scholars of great significance and value to literature. This forms a part of the knowledge base for future generations. So that the book is never forgotten we have represented this book in a print format as the same form as it was originally first published. Hence any marks or annotations seen are left intentionally to preserve its true nature.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Supervision is an essential constituent of analytic and psychotherapy training and a crucial part of ongoing professional development for all practitioners. In spite of this, little formal theory about supervision has been developed and, for the most part, learning to supervise has progressed using a simple apprenticeship model. Supervising and Being Supervised aims to rectify this situation. Jan Wiener, Richard Mizen and Jenny Duckham draw together contributions from a number of experienced Jungian analysts who supervise to explore key aspects of the supervisory experience with the aim of developing a theory for analytically-based work. Part One explores the nature of the supervisor-supervisee relationship, Part Two looks at a number of the settings and applications of supervision and Part Three examines problems that might occur in supervision. In the fourth and final part, and drawing on the previous chapters, the focus turns specifically to the challenges of developing a clear theory of supervision.

This incredibly useful guide book to mathematics contains the fundamental working knowledge of mathematics which is needed as an everyday guide for working scientists and engineers, as well as for students. Now in its fifth updated edition, it is easy to understand, and convenient to use. Inside you’ll find the information necessary to evaluate most problems which occur in concrete applications. In the newer editions emphasis was laid on those fields of mathematics that became more important for the formulation and modeling of technical and natural processes. For the 5th edition, the chapters "Computer Algebra Systems" and "Dynamical Systems and Chaos" have been revised, updated and expanded.