English | 2019 | ISBN: 1097417117 | 146 Pages | PDF | 17 MB
Machine learning can be described as a form of statistical analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data. This book is written for sophomore students in computer science, technology, engineering, or mathematics (STEM), assuming that they know algebra and calculus. Readers should have already solved some problems using computer programs. More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into. The book discusses many methods that have their bases in different fields: statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. In the past, research in these different communities followed different paths with different emphases. In this book, the aim is to incorporate them together to give a unified treatment of the problems and the proposed solutions to them. This is an introductory textbook, intended for senior undergraduate and graduate-level courses on machine learning, as well as engineers working in the industry who are interested in the application of these methods. The prerequisites are courses on computer programming, probability, calculus, and linear algebra. The aim is to have all learning algorithms sufficiently explained so it will be a small step from the equations given in the book to a computer program. For some cases, pseudocode of algorithms are also included to make this task easier. I very much enjoyed writing this book; I hope you will enjoy reading it.