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The Mathematics of Machine Learning
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.
Machine Learning with Python
This book explains how to use the programming language Python to develop machine learning and deep learning tasks.
Approximation and Stability Properties of Numerical Methods for Hyperbolic Conservation Laws
The book focuses on stability and approximation results concerning recent numerical methods for the numerical solution of hyperbolic conservation laws. The work begins with a detailed and thorough introduction of hyperbolic conservation/balance laws and their numerical treatment. In the main part, recent results in such context are presented focusing on the investigation of approximation properties of discontinuous Galerkin and flux reconstruction methods, the construction of (entropy) stable numerical methods and the extension of existing (entropy) stability results for both semidiscrete and fully discrete schemes, and development of new high-order methods.
Exploring the Implications of Complexity Thinking for Translation Studies
Exploring the Implications of Complexity Thinking for Translation Studies considers the new link between translation studies and complexity thinking. Edited by leading scholars in this emerging field, the collection builds on and expands work done in complexity thinking in translation studies over the past decade. In this volume, the contributors address a variety of implications that this new approach holds for key concepts in Translation Studies such as source vs. target texts, translational units, authorship, translatorship, for research topics including translation data, machine translation, communities of practice, and for research methods such as constraints and the emergence of trajectories. The various chapters provide valuable information as to how research methods informed by complexity thinking can be applied in translation studies. Presenting theoretical and methodological contributions as well as case studies, this volume is of interest to advanced students, academics, and researchers in translation and interpreting studies, literary studies, and related areas.
Principles and Practice of Fertility Preservation
The specialty of fertility preservation offers patients with cancer, who are rendered infertile by chemo- and radiotherapy, the opportunity to realize their reproductive potential. This gold-standard publication defines the specialty. The full range of techniques and scientific concepts is covered in detail, and the author team includes many of the world's leading experts in the field. The book opens with introductions to fertility preservation in both cancer and non-cancer patients, followed by cancer biology, epidemiology and treatment, and reproductive biology and cryobiology. Subsequent sections cover fertility preservation strategies in males and females, including medical/surgical procedures, ART, cryopreservation and transplantation of both ovarian tissue and the whole ovary, and in-vitro follicle growth and maturation. Concluding chapters address future technologies, as well as ethical, legal and religious issues. Richly illustrated throughout, this is a key resource for all clinicians specializing in reproductive medicine, gynecology, oncology, hematology, endocrinology and infertility.
Machine Learning
Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning
Translation Studies at the Interface of Disciplines
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Current Catalog
First multi-year cumulation covers six years: 1965-70.
Pandemic Exposures
For people and governments around the world, the onset of the COVID-19 pandemic seemed to place the preservation of human life at odds with the pursuit of economic and social life. Yet this simple alternative belies the complexity of the entanglements the crisis has created and revealed, not just between health and wealth but also around morality, knowledge, governance, culture, and everyday subsistence. Didier Fassin and Marion Fourcade have assembled an eminent team of scholars from across the social sciences, conducting research on six continents, to reflect on the multiple ways the coronavirus has entered, reshaped, or exacerbated existing trends and structures in every part of the globe. The contributors show how the disruptions caused by the pandemic have both hastened the rise of new social divisions and hardened old inequalities and dilemmas. An indispensable volume, Pandemic Exposures provides an illuminating analysis of this watershed moment and its possible aftermath.
