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 homework and exam
questions:
Check out the
homework assignments and exam questions
from the Fall 1998 CMU Machine
Learning course (also includes pointers to earlier and later offerings
of the
course).
Additional tutorial
materials:
Support Vector Machines: