cm

February
2010: ISBN-10: 0-262-01243-X, ISBN-13:
978-0-262-01243-0

The book can be ordered
through The MIT
Press, Amazon (CA, CN, DE, FR, JP, UK, US), Barnes&Noble (US), Pandora (TR).

·
PHI
Learning Pvt. Ltd. (formerly Prentice-Hall of India) published an English
language reprint
for distribution in India, Bangladesh, Burma, Nepal, Sri Lanka, Bhutan, and
Pakistan only.

·
Yapay Öğrenme,
the Turkish edition of the book (translated by the author) was published by
Boğaziçi University Press in April 2011.

·
Chinese
simplified character edition of the book will be published by China Machine
Press/Huazhang Graphics & Information Co.

Table of Contents and Sample Chapters

**Lecture Slides:**

*(For instructors to use in their
courses; please keep the first page and footer if you edit the slides)*

- Introduction (pdf,ppt)
- Supervised Learning (pdf, ppt)
- Bayesian Decision Theory (pdf, ppt)
- Parametric Methods (pdf, ppt)
- Multivariate Methods (pdf, ppt)
- Dimensionality Reduction (pdf, ppt)
- Clustering (pdf, ppt)
- Nonparametric Methods (pdf, ppt)
- Decision Trees (pdf, ppt)
- Linear Discrimination (pdf, ppt)
- Multilayer Perceptrons (pdf, ppt)
- Local Models (pdf, ppt)
- Kernel Machines (pdf, ppt)
- Bayesian Estimation (pdf, ppt)
- Hidden Markov Models (pdf, ppt)
- Graphical Models (pdf, ppt)
- Combining Multiple Learners (pdf, ppt)
- Reinforcement Learning (pdf, ppt)
- Design and Analysis of Machine Learning Experiments (pdf, ppt)

**For Instructors:** Select the "Online
Instructor's Manual and Supplemental Content Download Request" link in the
left menu of the book's web page, http://mitpress.mit.edu/

The goal of machine learning is to
program computers to use example data or past experience to solve a given
problem. Many successful applications of machine learning exist already,
including systems that analyze past sales data to predict customer behavior,
optimize robot behavior so that a task can be completed using minimum
resources, and extract knowledge from bioinformatics data. *Introduction to
Machine Learning* is a comprehensive textbook on the subject, covering a
broad array of topics not usually included in introductory machine learning
texts. In order to present a unified treatment of machine learning problems and
solutions, it discusses many methods from different fields, including
statistics, pattern recognition, neural networks, artificial intelligence,
signal processing, control, and data mining. All learning algorithms are
explained so that the student can easily move from the equations in the book to
a computer program.

The text covers such topics as supervised learning,
Bayesian decision theory, parametric methods, multivariate methods, multilayer
perceptrons, local models, hidden Markov models, assessing and comparing
classification algorithms, and reinforcement learning. **New to the second edition are chapters on kernel machines, graphical
models, and Bayesian estimation; expanded coverage of statistical tests in a
chapter on design and analysis of machine learning experiments; case studies
available on the Web (with downloadable results for instructors); and many
additional exercises. All chapters have been revised and updated.**

*Introduction to Machine Learning* can be used by
advanced undergraduates and graduate students who have completed courses in
computer programming, probability, calculus, and linear algebra. It will also
be of interest to engineers in the field who are concerned with the application
of machine learning methods.

**Errata:**

·
p. 41: Fourth line
from the bottom of the page: “ic” should be “is” (Alexander Moriarty)

·
p. 66: Fourth line
from the top of the page: “negligible” is misspelled (Bugra Akyildiz)

·
p. 124: Eq. 6.20;
subscript of \epsilon should be *j*
(Gi-Jeong Si)

·
p. 130: Below Eq.
6.37, while taking the derivative, 2 should be outside the parenthesis (Ali
Çeliksu, Gi-Jeong Si).

·
p. 135: Eq. 6.47;
in the final ** z**,

·
p. 194: Eq. 9.15: *b _{m}* should be

·
p. 224: Just above Eq.
10.30, after Mult, the subscript *k*
should be uppercase *K* (Gi-Jeong Si)

·
p. 283: Around the
middle of the page, it should be: *l* not equal to *j *(Gi-Jeong Si)

·
p. 319: In the
constraints below Eq. 13.19, it should be \sum_t \alpha_t \ge \nu (Rui Kuang)

·
p. 330: In the
third line of the first equation, the + before (*w*^{T}** x**
+ w

·
p. 330: In Eq.
13.50, the – sign before the last term (\sum_t r^t(\alpha^t+\alpha^t_-) )
should be a + (Yongwoon Cho)

·
p. 333: Just under
Eq. 13.53, *t* of \gamma should be a
superscript. (Gi-Jeong Si)

·
p. 336: Eq in the
middle of the page; subscript of \lambda should be *j* (Gi-Jeong Si)

·
p. 343: Two lines
before the bottom of the page, the subscript of the last *q* should be uppercase *K*
(Gi-Jeong Si)

·
p. 348: Third eq on
the page, the correct way to write is L(** w**|

·
p. 348: Eq 14.11:
The second term should read N\log\sqrt{\beta} (Orhan Özalp)

·
p. 352: 7^{th}
line from the top of the page, closing ] is missing after 1,0 (Gi-Jeong Si)

·
p. 356: First eq. *p*(** x**) should be

·
p. 378: Eq. 15.33:
There should be a normalizing 1/P(*O ^{k}*)
factor after sum over

·
P. 389: The very
last eq on the bottom of the page; the prob is 0.48 and not 0.47 (Gökhan
Özbulak)

·
p. 392: The first
equation, the denominator of the second term; there should be no ~ (Gi-Jeong
Si)

·
p. 405: Second line
of Eq. 16.17: Index of summation should be Y in the second summation (Alex
Kogan)

·
p. 492: Two lines
below Eq. 18.20; the – between *r _{t}*

·
p. 500: The
denominator should be divided by N (inside sqrt): \sqrt{p_0(1-p_0)/N} (Lisa
Hellerstein)

*I
would like to thank everyone who took the time to find these errors and report
them to me.*

*Created on Feb 11,
2010 by E. Alpaydin (my_last_name AT boun DOT edu DOT tr)*

*March 4, 2010: Added lecture slides and solutions of exercises for instructors.**May 20, 2010: Added info about the reprint by PHI Learning Pvt. Ltd of India.**June 20, 2011: Added info about the Turkish edition.**Sep 26, 2011: Added lecture slides and errata.**May 22, 2012: Changed instructor’s kit link to MIT website.**Apr 5, 2013: Added info about the Chinese simplified character edition.*