Concise Introduction to Machine 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.

• Introduction. Probability Theory. Sampling. Linear Classification. Non-Linear Classification. Dimensionality Reduction. Regression. Feature Learning.

"This book aims to present a concise yet rigorous introduction to several basic topics in machinelearning. The concepts and algorithms are comprehensively explained with intuition and illustrative examples in MATLAB, using mathematics as the common language. The focus is onwhy and how an algorithm works...this book covers the mathematical foundation, the techniques and applications in machine learning well. It may be useful for readers with some background in mathematics who wish to extend themselves in statistics and machine learning, such as statisticians, graduate and senior undergraduate students."-- Shuangzhe Liu, Professor, University of Canberra