A Concise Introduction to Machine Learning

這本書的重點在於「為什麼」- 只有當我們了解一個演算法成功的原因，才能正確應用它並信任其結果。演算法通常被並列教授，但卻沒有顯示它們之間的相似性和差異性。這本書探討了這些共同點，旨在提供全面深入的解析，並培養直覺，同時保持簡潔。

這本有用的參考書應該是任何應用機器學習技術的人必備的書籍之一。

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.

A.C. Faul是劍橋大學Selwyn學院的數學教學助理、研究員和研究主任。她在德國學習了兩年後來到劍橋。她在劍橋的Churchill學院修讀了第二部分和第三部分的數學課程。由於這只有兩年，而第一學位需要三年，所以她沒有取得學位。然而，在Mike Powell教授的指導下，她獲得了有關Faul-Powell算法的博士學位，該算法用於徑向基函數插值。之後，她在Microsoft Research Cambridge與Mike Tipping合作研究了相關向量機。接下來的十年中，她在工業界工作，從事移動電話網絡、圖像處理和數據可視化等各種算法的研究。目前的項目是關於機器學習技術。在教學方面，她喜歡揭示算法的基本連接原則，這也是她撰寫的《數值分析》一書的重點。