Concise Introduction to Machine Learning [Minkštas viršelis]

(University of Cambridge, UK)
  • Formatas: Paperback / softback, 314 pages, aukštis x plotis: 235x156 mm, weight: 508 g, 15 Tables, black and white; 123 Illustrations, black and white
  • Išleidimo metai: 07-Aug-2019
  • Leidėjas: CRC Press Inc
  • ISBN-10: 0815384106
  • ISBN-13: 9780815384106
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 314 pages, aukštis x plotis: 235x156 mm, weight: 508 g, 15 Tables, black and white; 123 Illustrations, black and white
  • Išleidimo metai: 07-Aug-2019
  • Leidėjas: CRC Press Inc
  • ISBN-10: 0815384106
  • ISBN-13: 9780815384106
Kitos knygos pagal šią temą:

Machine Learning is known by many different names, and is used in many areas of science. It is also used for a variety of applications, including spam filtering, optical character recognition, search engines, computer vision, NLP, advertising, fraud detection, robotics, data prediction, astronomy. Considering this, it can often be difficult to find a solution to a problem in the literature, simply because different words and phrases are used for the same concept. This class-tested textbook aims to alleviate this, using mathematics as the common language. It covers a variety of machine learning concepts from basic principles, and llustrates every concept using examples in MATLAB.

Chapter
1. Introduction
Chapter
2. Probability Theory
Chapter
3. Sampling
Chapter
4. Linear Classification
Chapter
5. Non-Linear Classification
Chapter
6. Dimensionality Reduction
Chapter
7. Regression
Chapter
8. Feature Learning
A.C. Faul was a Teaching Associate, Fellow and Director of Studies in Mathematics at Selwyn College, University of Cambridge. She came to Cambridge after studying two years in Germany. She did Part II and Part III Mathematics at Churchill College, Cambridge. Since these are only two years, and three years are necessary for a first degree, she does not hold one. However, this was followed by a PhD on the Faul-Powell Algorithm for Radial Basis Function Interpolation under the supervision of Professor Mike Powell. She then worked on the Relevance Vector Machine with Mike Tipping at Microsoft Research Cambridge. Ten years in industry followed where she worked on various algorithms on mobile phone networks, image processing and data visualization. Current projects are on machine learning techniques. In teaching, she enjoys to bring out the underlying, connecting principles of algorithms, which is the emphasis of a book on Numerical Analysis she has written.