NYC Predictive Analytics Message Board Resources › Resource: Reading List

Resource: Reading List

Alex L.
user 9260860
Group Organizer
New York, NY
Post #: 4
From time to time we get asked for a reading list of resources about predictive analytics, so here it is...finally!!

Predictive analytics draws on a wide and deep pool of knowledge coming from established and always-evolving fields such as machine learning, data mining, and statistical methods, and therefore many of the books listed below come from these categories.

Although this is a pretty comprehensive list containing many well known texts, we know there are many more. If you have any resources to recommend, please share them with the Group.

General Intro Books:

Programming Collective Intelligence: Building Smart Web 2.0 Applications
by Toby Segaran
http://www.amazon.com...­

Collective Intelligence in Action
by Satnam Alag
http://www.amazon.com...­

Algorithms of the Intelligent Web
by Haralambos Marmanis and Dmitry Babenko
http://www.amazon.com...­

Technical Book List:

Machine Learning:
Pattern Recognition and Machine Learning
by Christopher M. Bishop
http://www.amazon.com...­

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
http://www.amazon.com...­
or download pdf version at http://www-stat.stanf...­

Artificial Intelligence, A Modern Approach. Third Edition. (2009). Prentice Hall.
by Russell & Norvig
The companion site is: http://aima.cs.berkel...­

Data Mining:
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
by Ian H. Witten and Eibe Frank
http://www.amazon.com...­

Data Mining: Concepts, Models, Methods, and Algorithms
by Mehmed Kantardzic
http://www.amazon.com...­

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
by Bing Liu
http://www.amazon.com...­

Statistical Modeling: The Two Worlds (PDF)
by Leo Brieman
Our 'big data' world needs tools/techniques that are are more flexible than those provided by classical statistics. This paper helped me understand statistics vs. exploratory data analysis, parametric vs. non-parametric modeling.

Data Mining Methods and Models
by Daniel T. Larose
This book includes problem sets and a case study on modeling response. Larose has other books that look useful but I have not checked them out yet.

Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
by Bruce Ratner
This book focuses on database response modeling.

Math:
Matrix Analysis and Applied Linear Algebra Book and Solutions Manual
by Carl D. Meyer
http://www.amazon.com...­

Mastering MATLAB 7
by Duane C. Hanselman and Bruce L. Littlefield
http://www.amazon.com...­

Natural Language Processing (NLP):
Foundations of Statistical Natural Language Processing
by Chris Manning and others
http://www.amazon.com...­

Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization
by Peter Jackson and others
http://www.amazon.com...­

Text Mining:
The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
by Ronen Feldman and others
http://www.amazon.com...­

Text Mining: Predictive Methods for Analyzing Unstructured Information
by Sholom M. Weiss and others
http://www.amazon.com...­

Text Mining: Classification, Clustering, and Applications
by Ashok Srivastava and others
http://www.amazon.com...­

Information Retrieval:
Introduction to Information Retrieval (online book)
by Chris Manning and others
http://nlp.stanford.e...­

Lucene in Action
by Otis Gospodnetic and Erik Hatcher
http://www.amazon.com...­

Programming Algorithm:
Mastering Algorithms with C
by Kyle Loudon
http://www.amazon.com...­

Numerical Recipes in C, Second Edition (1992) (online book)
http://www.nr.com/old...­

SIMD Programming Manual for Linux and Windows
by Paul Cockshott and Kenneth Renfrew
http://www.amazon.com...­

Specific Subject Area:
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
by Nello Cristianini and John Shawe-Taylor
http://www.amazon.com...­

Understanding Complex Datasets: Data Mining with Matrix Decompositions
by David B. Skillicorn
http://www.amazon.com...­

Independent Component Analysis: A Tutorial Introduction
by James V. Stone
http://www.amazon.com...­
Otis G.
otisg
Brooklyn, NY
Post #: 9
Nice list.



Otis
Alex L.
user 9260860
Group Organizer
New York, NY
Post #: 5
Thanks Chris Tang for this nice online book about Opinion Mining.
(http://www.cs.cornell...­)
Opinion mining and sentiment analysis
A former member
Post #: 1
I got into data mining and predictive analytics from the biz/web analytics side of things. I would recommend that any analyst looking to stay current look into the concept of "exploratory data analysis", of which classical statistics is a part

Statistical Modeling: The Two Worlds (PDF)
by Leo Brieman
Our 'big data' world needs tools/techniques that are are more flexible than those provided by classical statistics. This paper helped me understand statistics vs. exploratory data analysis, parametric vs. non-parametric modeling.

Data Mining Methods and Models
by Daniel T. Larose
This book includes problem sets and a case study on modeling response. Larose has other books that look useful but I have not checked them out yet.

Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
by Bruce Ratner
This book focuses on database response modeling.
John S.
user 8114456
Brooklyn, NY
Post #: 1
Shall we also add the great grandaddy of them all? Once you start it, it's hard to put down since it's so good at interlinking the topics. Third edition has a nicely improved chapter that introduces machine learning.

Russell & Norvig, Artificial Intelligence, A Modern Approach. Third Edition. (2009). Prentice Hall.

The companion site is: http://aima.cs.berkel...­

A former member
Post #: 3
John, thanks for sharing that resource, I'm a sucker for books that are good at interlinking topics! We have updated the reading list to include your suggestion. If you have any ideas for future meetup topics, or know of anyone who might be interested in presenting, please let us know! See you on April 1.
Binesh
user 9228524
New York, NY
Post #: 2
Just found this message.. Here are my favorites:


  • Pattern Classification
    by Richard O. Duda, Peter E. Hart, David G. Stork
    http://www.amazon.com...­
  • Reinforcement Learning: An Introduction
    Richard S. Sutton, Andrew G. Barto
    http://www.amazon.com...­
  • Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
    Russell D. Reed, Robert J. Marks II
  • Genetic Programming: An Introduction
    Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, Frank D. Francone
    http://www.amazon.com...­


(It's hard to pick favorites. So, I won't, I loved them all. But, Neural Smithing in particular had some information that I've found rare in a lot of other books (perhaps any). Specifically, it had sort of hints about learning rates, momentum, decay rates, sigmoid saturation, etc in neural networks, that seemed hard to get, unless borne of experience. The hints were backed up by solid experiment and results. Also, entire section on (The rest are fairly straightforward descriptions of the algorithms, and the particular "intuitions" of the techniques are left to you. That's not necessarily criticisms of the other books, so much as it is extraordinary of Neural Smithing.))
A former member
Post #: 1
Your link to Leo Breiman is dead. If you can find the reference, let me know.
Powered by mvnForum

People in this
Meetup are also in:

Sign up

Meetup members, Log in

By clicking "Sign up" or "Sign up using Facebook", you confirm that you accept our Terms of Service & Privacy Policy