Название: Applied Machine Learning Автор: David Forsyth Издательство: Springer Год: 2019 Страниц: 493 Язык: английский Формат: pdf (true) Размер: 10.1 MB
Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code.
A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).
Here’s what you should know from that book: • Various descriptive statistics (mean, standard deviation, variance) and visualization methods for 1D datasets • Scatter plots, correlation, and prediction for 2D datasets • A little discrete probability • A very little continuous probability (rough grasp of probability density functions and how to interpret them) • Random variables and expectations • A little about samples and populations • Maximum likelihood • Simple Bayesian inference • A selection of facts about an assortment of useful probability distributions, or where to look them up
Programming Background: You should be able to pick up a practical grasp of a programming environment without too much fuss. I use either R or MATLAB for this sort of thing, depending on how reliable I think a particular package is. You should, too. In some places, it’s a good idea to use Python.
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