30-07-2011, 11:35 AM
[attachment=14938]
Preface
These notes are in the process of becoming a textbook. The process is quite
un nished, and the author solicits corrections, criticisms, and suggestions from
students and other readers. Although I have tried to eliminate errors, some un-
doubtedly remain|caveat lector. Many typographical infelicities will no doubt
persist until the nal version. More material has yet to be added. Please let Some of my plans for additions and
other reminders are mentioned in
marginal notes. me have your suggestions about topics that are too important to be left out.
I hope that future versions will cover Hop eld nets, Elman nets and other re-
current 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 sucient 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 P
eger, Robert Allen, and Lise Getoor.
Preface
These notes are in the process of becoming a textbook. The process is quite
un nished, and the author solicits corrections, criticisms, and suggestions from
students and other readers. Although I have tried to eliminate errors, some un-
doubtedly remain|caveat lector. Many typographical infelicities will no doubt
persist until the nal version. More material has yet to be added. Please let Some of my plans for additions and
other reminders are mentioned in
marginal notes. me have your suggestions about topics that are too important to be left out.
I hope that future versions will cover Hop eld nets, Elman nets and other re-
current 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 sucient 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 P
eger, Robert Allen, and Lise Getoor.