Machine Learning, Tom Mitchell, McGraw Hill

Book Description:

Machine Learning is the study of computer algorithms that improve automatically through experience. Applications range from datamining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests.

This book provides a single source introduction to the field. It is written for advanced undergraduate and graduate students, and for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed

Slides for instructors:

The following slides are made available for instructors teaching from the textbook Machine Learning, Tom Mitchell, McGraw-Hill.

Slides are available in both postscript, and in latex source. If you take the latex, be sure to also take the accomanying style files, postscript figures, etc.

• Ch 1. Introduction. ( postscript 3.8Meg), ( gzipped postscript 317k) (pdf ) ( latex source )
• Ch 2. Concept Learning. ( postscript 347k), ( gzipped postscript 100k) (pdf ) ( latex source )
• Ch 3. Decision Tree Learning. ( postscript 530k), ( gzipped postscript 143k) (pdf ) ( latex source )
• Ch 4. Artificial Neural Networks. ( postscript 1.83Meg), ( gzipped postscript 329k) (pdf ) ( latex source )
• Ch 5. Evaluating Hypotheses. ( postscript 212k), ( gzipped postscript 67k) (pdf ) ( latex source )
• Ch 6. Bayesian Learning. ( postscript 261k), ( gzipped postscript 81k) (pdf ) ( latex source )
  see also slides on learning Bayesian networks by Friedman and Goldszmidt.
• Ch 7. Computational Learning Theory. ( postscript 160k), ( gzipped postscript 50k) (pdf ) ( latex source )
• Ch 8. Instance Based Learning. ( postscript 138k), ( gzipped postscript 39k) (pdf ) ( latex source )
• Ch 9. Genetic Algorithms. ( postscript 245k), ( gzipped postscript 72k) (pdf ) ( latex source )
• Ch 10. Learning Sets of Rules. ( postscript 185k), ( gzipped postscript 57k) (pdf ) ( latex source )
• Ch 11. Analytical Learning. ( postscript 261k)  (pdf )   ( latex source ) 
• Ch 12. Combining Inductive and Analytical Learning. ( postscript 419k), ( gzipped postscript 103k) (pdf ) ( latex source )
• Ch 13. Reinforcment Learning. ( postscript 172k), ( gzipped postscript 40k) (pdf ) ( latex source )

Additional tutorial materials:

Support Vector Machines:

• Tutorial information on Support vector machines
• Freeware implementation : SVM Light by Thorsten Joachims.
• K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Neural Networks, 12(2):181-201, May 2001. (PDF)