Prerequisites: STAT 509 (Statistics for Engineers) or 510 (Introduction to Applied Probability) or 511 (Probability)
Meeting Time and Place: TTh 930-1045, Sumwalt 241.
Instructor: Marco Valtorta
Office: Sumwalt 206A, 777-4879
Office Hours: Mon 3-5, Wed 3-5,
Grader: No grader for this course
The goals of this course, as adapted from the old departmental syllabus, are:
The precise grading policy has not yet been determined. The course grade will be based on homework, a midterm exam (or possibly two midterm exams), a final exam, possible quizzes. The homework consists of both paper and pencil exercises and computer exercises using Maple. A final project involves the analysis of a data set using a classifier such as Parzen windows, the k-nearest-neighbors method, etc. While the final project can be carried out using Maple, most students in the past found it more convenient to implement the algorithm using a traditional imperative programming language such as C.
Final Exam (take-home)
Oral Exam Schedule
Syllabus and Required Texts
Here is the reference to another text on statistical pattern recognition: Fukunaga, Keinosure. Introduction to Statistical Pattern Recognition (second edition). Academic Press, 1990. ISBN: 0-12-269851-7.
Some useful links:
A talk on hidden Markov models that emphasizes the connection to Bayesian nets (in postscript).
How to view postscript in Windows: Wim Sweldens's web page on GSview.
A handout with Maple examples related to Exercise 14, Ch.2 DHS01.
An introduction to Maple: Professors Miller and Meade's "Day 1" lecture.
A Talk by Kathy Laskey: Bayesian Decision Theory and Machine Learning.