Decolonizing Methodologies


Book Description

'A landmark in the process of decolonizing imperial Western knowledge.' Walter Mignolo, Duke University To the colonized, the term 'research' is conflated with European colonialism; the ways in which academic research has been implicated in the throes of imperialism remains a painful memory. This essential volume explores intersections of imperialism and research - specifically, the ways in which imperialism is embedded in disciplines of knowledge and tradition as 'regimes of truth.' Concepts such as 'discovery' and 'claiming' are discussed and an argument presented that the decolonization of research methods will help to reclaim control over indigenous ways of knowing and being. Now in its eagerly awaited second edition, this bestselling book has been substantially revised, with new case-studies and examples and important additions on new indigenous literature, the role of research in indigenous struggles for social justice, which brings this essential volume urgently up-to-date.




ORYZA2000


Book Description










Computational Complexity


Book Description

New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.




Book Auction Records


Book Description

A priced and annotated annual record of international book auctions.




Data Science and Machine Learning


Book Description

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code




All of Statistics


Book Description

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.