Instructor:: Dr. Harris Prereqs: previous programming experience and knowledge of linear algebra and statistics Lecture: 8.5 Time Consumption: 8.0 Student Performance: Evaluated: Fall 97 Response: 16 Percent:
Texts: Neural Networks for Pattern Recognition, Christopher M. Bishop, Oxford University Press, 1995
Reference:
Review: Statistics, Linear Algebra, and Matlab
Note:
Decision functions; optimum decision criteria; training algorithms; unsupervised learning; feature extraction, data reduction; potential functions; syntactic pattern description; recognition grammars; machine intelligence.
There were a lot of in class discussions that tended to be informative. Most of the students thought the examples given in class were helpful and relevant not just to understanding of the material but also to the homework. In fact, several students thought the best thing about the class was the teacher. One student did say though that the worst thing about the class was "Matlab (a necessary evil)".
The pace of the course was fast; however, all of the students thought it was a good pace. The average student spent about 10 hours each week doing homework and reading.
"This course is really fun." "Be an expert programmer in Matlab." "Practice your Matlab and linear algebra."