List of Courses

Information
Description
Outcomes
Contents
Methodologies
Assessment
Books

Machine Learning and Pattern Recognition

Course Code :
EED472
Level :
Undergraduate
Course Hours :
3.00 Hours
Department :
Communication and Computer Engineering

Instructor information :

Area of Study :

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Machine Learning and Pattern Recognition

Introduction to Machine Learning: Concepts, Instances, Attributes, Simple Examples, Application Domains. Machine Learning and Statistics. Data Pre-processing and Exploration: Sampling, Principal Component Analysis, Feature Extraction, Exploratory Data Analysis. Fundamental Classification Strategies. Clustering Techniques. Statistical and structural pattern recognition approaches. Bayesian decision theory. MaximumLikelihood and Bayesian parameter estimation. Nearest neighbour rule. Non-parametric classifiers. Linear discriminate functions. Non-linear classifiers. Multi-layer neural networks. Features selection. Template matching. Unsupervised learning and Cluster analysis. Supervised learning.

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Machine Learning and Pattern Recognition

Course outcomes:

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Machine Learning and Pattern Recognition

Course topics and contents:

Topic	No. of hours	Lecture	Tutorial/Practical

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Machine Learning and Pattern Recognition

Teaching And Learning Methodologies:

Teaching and learning methods

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Machine Learning and Pattern Recognition

Course Assessment :

Methods of assessment	Relative weight %	Week No.	Assess What

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Machine Learning and Pattern Recognition

Books:

Book	Author	Publisher

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

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