Pattern Recognition (EE 448) Course Details

Course Name: Pattern Recognition
Code: EE 448
Pre-requisite Course(s): Knowledge of Linear Algebra, Probability Theory, Matlab
Objective: 1. Instill in the students an understanding of where Pattern Recognition sits in the hierarchy of artificial intelligence and soft computing techniques 2. Develop expertise in various unsupervised learning algorithms such as clustering techniques (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation 3. Provide the student with the ability to apply these techniques in exploratory data analysis
Content: Introduction to the theory of pattern recognition, Bayesian decision theory, Maximum likelihood estimation, Nonparametric estimation, Linear discriminant functions, Support vector machines, Neural networks, Unsupervised learning and Clustering, Applications such as handwriting recognition, lipreading, geological analysis, medical data processing, data mining, information retrieval, human-computer interaction
Term: Spring
Theory: 3
Application: 0
Laboratory: 0
Credit: 3
Web:
ECTS Course File: Course File
Course File: Course File
ECTS: 5