Introduction to Machine Learning - Nils Nilsson

These notes are in the process of becoming a textbook. The process is quite unfinished, and the author solicits corrections, criticisms, and suggestions from students and other readers. Although I have tried to eliminate errors, some undoubtedly remain—caveat lector. Many typographical infelicities will no doubt persist until the final version. More material has yet to be added. Please let Some of my plans for additions and other reminders are mentioned in me have your suggestions about topics that are too important to be left out. marginal notes.

I hope that future versions will cover Hopfield nets, Elman nets and other recurrent nets, radial basis functions, grammar and automata learning, genetic algorithms, and Bayes networks . . .. I am also collecting exercises and project suggestions which will appear in future versions.

My intention is to pursue a middle ground between a theoretical textbook and one that focusses on applications. The book concentrates on the important ideas in machine learning. I do not give proofs of many of the theorems that I state, but I do give plausibility arguments and citations to formal proofs. And, I do not treat many matters that would be of practical importance in applications; the book is not a handbook of machine learning practice. Instead, my goal is to give the reader sufficient preparation to make the extensive literature on machine learning accessible.

Students in my Stanford courses on machine learning have already made several useful suggestions, as have my colleague, Pat Langley, and my teaching assistants, Ron Kohavi, Karl Pfleger, Robert Allen, and Lise Getoor.

Introduction to Machine Learning

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