NUST - PG Courses

RIME-942 Pattern Recognition (3-0)

Campus	SMME
Programs	PG
Session	Fall Semester 2016
Course Title	Pattern Recognition (3-0)
Course Code	RIME-942
Credit Hours	3
Pre-Requisutes	None
Course Objectives	This course will introduce students to the fundamentals of statistical pattern recognition with examples from several application areas. Techniques for analyzing multidimensional data of various types and scales along with algorithms for projection, dimensionality reduction, clustering and classification of data will be explained. The course will present competing approaches to exploratory data analysis and classifier design so that students can make judicious choices when confronted with real pattern recognition problems. Students will implement some of the algorithms using their choice of a programming language.
Detail Content	Introduction to Pattern Recognition Statistical Decision Theory Parameter Estimation Curse of Dimensionality Component analysis and Discriminants Principle Component Analysis (PCA) Nonparametric Techniques Linear Discriminant Functions Decision Trees Neural Networks Error Rate Estimation, Bagging, Boosting Classifier Combination Feature Selection Unsupervised Learning, Clustering, and Multidimensional Scaling Semi-supervised Learning Course Outcomes Students graduating from this course are expected to develop a deep insight into cutting edge artificial intelligence technologies with an overview of the underlying theory and methodologies in each subject area. This course will be very helpful for students to take up research theses of good technical value in the cutting edge research areas.
Text/Ref Books	“Pattern Recognition and Machine Learning” by Christopher M. Bishop, Springer; 1st ed. 2006, Corr. 2nd printing edition 2007. “Pattern Classification” by Richard O. Duda, Peter E. Hart and David G. Stork, Second Edition, Wiley, 2001.
Time Schedule
Faculty/Resource Person	Asst Prof Dr M Naveed