Semester:
Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Recommended Background:
CSE 330 and MTH 314 and STT 441
Restrictions:
Open only to Computer Science or Electrical Engineering majors.
Description:
Algorithms for classifying and understanding data. Statistical and syntactic methods, supervised and unsupervised machine learning. Cluster analysis and ordination. Exploratory data analysis. Methodology for design of classifiers.
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
(CSE 331 and MTH 314 and STT 441) or CSE 331 and MTH 314 and STT 441
Restrictions:
Open to graduate students in the Department of Computer Science and Engineering or in the Department of Electrical and Computer Engineering.
Description:
Algorithms for classifying and understanding data. Statistical and syntactic methods, supervised and unsupervised machine learning. Cluster analysis and ordination. Exploratory data analysis. Methodology for design of classifiers.
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
(CSE 331 and MTH 314 and STT 441) or CSE 331 and MTH 314 and STT 441
Restrictions:
Open to graduate students in the Department of Computer Science and Engineering or approval of department.
Description:
Introduction to salient topics in statistical pattern recognition. These include concepts in Bayesian decision theory, parametric and non-parametric density estimation schemes, linear discriminant functions, perceptrons and unsupervised clustering. The project component of this course will test the student's ability to design and evaluate classifiers on datasets.