Books
Information about checking out Reserve books - Updated 2/4/2011
Comments added below each book, 2/4/2011.
The books on reserve for the course have two-hour/overnight loan privileges. This means the following:
- During the day, they may be checked our for in-library use for two hours.
- Two hours before the library closes they may be checked out overnight. You can see the library hours at the top right of the homepage, http://www.lib.neu.edu/
- On most days this would mean that they could be checked out after 9:45PM.
- They must be returned within one hour after the library opens, which is usually 7:45AM. So they have to be returned by 8:45AM.
The custom text for the course is on Reserve, in addition to the following:
- Akmajian, A. (2010). Linguistics : an introduction to language and communication (6th ed.). Cambridge, Mass.: MIT Press.
A very readable introduction to linguistics (not computational linguistics). - Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python: O'Reilly Media. (an e-book)
A thorough introduction to the leading Natural Language Toolkit (NLTK). Walks you through it in easy steps. You can do a lot with this without knowing Python or doing any Python programming. This is an e-book in our library, so multiple readers can access it simultaneously. - Bolstad, W. M. (2007). Introduction to Bayesian statistics (2nd ed.). Hoboken, N.J.: John Wiley.
This comes highly recommended, but I know little about it. - Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing (2nd ed.). Upper Saddle River, NJ: Prentice-Hall. (two copies on reserve)
This is the leading textbook on computational linguistics. Quite readable. - Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. New York: Cambridge University Press.
A major application of computational linguistics is information retrieval. This is an excellent and up-to-date book on the topic. - Manning, C. D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, Massachusetts: MIT Press.
This is the leading book on computational linguistics that emphasizes statistical approaches. The math gets thick after a while, but even if you skip the hairy details, you can still learn a lot about the topic. - Mitchell, T. M. (1997). Machine Learning. New York: McGraw-Hill.
For years, this was the leading textbook introduction to machine learning. The basic principles it describes are still in play, though it does leave out some modern developments. - Poole, D. L., & Mackworth, A. K. (2010). Artificial intelligence : Foundations of computational agents. New York: Cambridge University Press.
This is another major text about AI. You'll find it a little more useable than the more advanced Russell and Norvig chapters selected for you custom textbook. - Russell, S. J., & Norvig, P. (2010). Artificial intelligence : A modern approach (3rd ed.). Upper Saddle River, N.J.: Prentice Hall/Pearson Education.
The leading AI textbook from which I selected chapters for our custom text. - Weiss, S. M. (2010). Fundamentals of predictive text mining (1st. ed.). New York: Springer. (an e-book)
Text mining is the process of analyzing unstructured natural language text -- how to extract information from documents. This is an introductory textbook on the topic. - Weiss, S. M., & Kulikowski, C. A. (1991). Computer Systems that Learn. San Mateo, CA: Morgan Kaufmann.
This is a great little book that introduces you to the basics of machine learning. - Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques: Morgan Kaufmann. (two copies on reserve)
This excellent book that discusses many aspects of machine learning. The authors
are the people who developed the most widely used machine learning software system,
Weka. So some of the book shows how to do machine learning using Weka.
Return to the CS4100 homepage.