1. Probabilistic Programming and Bayesian Methods for Hackers
Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
2. Bayesian Reasoning and Machine Learning
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master’s students with limited background in linear algebra and calculus. “With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying web site, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Only students not included.” Jaakko Hollmén, Aalto University.
3. Deep Learning
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” ―Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
4. Matrix Computations:
This 2013 edition by Golub and Van Loan, published by The Johns Hopkins University Press. Classic text in computer science provides essential information about the mathematical background and algorithmic skills required for the production of numerical software. This edition includes thoroughly revised chapters on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating point arithmetic, a more accurate rendition of the modified Gram-Schmidt process, and new material devoted to GMRES, QMR, and other methods designed to handle the sparse unsymmetric linear system problem.
5. A Probabilistic Theory of Pattern Recognition
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
6. Advanced engineering mathematics
Advanced Engineering Mathematics, 10th Edition is known for its comprehensive coverage, careful and correct mathematics, outstanding exercises, and self-contained subject matter parts for maximum flexibility. The new edition continues with the tradition of providing instructors and students with a comprehensive and up-to-date resource for teaching and learning engineering mathematics, that is, applied mathematics for engineers and physicists, mathematicians and computer scientists, as well as members of other disciplines
7. Probability and Statistics Cookbook:
This cookbook integrates a variety of topics in probability theory and statistics. It is based on literature and in-class material from courses of the statistics department at the University of California in Berkeley but also influenced by other sources.
8. An Introduction to Statistical Learning (with applications in R):
This book provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.