A Concise Introduction to Machine Learning
暫譯: 機器學習簡明導論

本書的重點在於「為什麼」這個問題——只有理解了為什麼一個演算法成功，才能正確地應用它，並信任其結果。演算法通常是並排教授，而不顯示它們之間的相似性和差異。本書針對這些共通點進行探討，旨在提供徹底且深入的處理，並培養直覺，同時保持簡潔。

這本有用的參考書應該是任何使用機器學習技術的人的書架上必備的書籍。

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 是劍橋大學塞爾溫學院的數學教學助理、研究員及學習主任。她在德國學習了兩年後來到劍橋。她在劍橋的丘吉爾學院完成了數學的第二部分和第三部分。由於這僅僅是兩年，而獲得第一學位需要三年，因此她並未獲得學位。然而，她隨後在邁克·鮑威爾教授的指導下，完成了關於 Faul-Powell 演算法在徑向基函數插值中的應用的博士學位。之後，她在微軟研究院劍橋分部與邁克·蒂平合作研究相關向量機。隨後的十年中，她在業界工作，專注於移動電話網絡、影像處理和數據可視化的各種演算法。目前的項目集中在機器學習技術上。在教學方面，她喜歡揭示演算法背後的基本連結原則，這也是她所撰寫的數值分析書籍的重點。