Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: DTS201TC
Module Title: Pattern Recognition
Module Level: Level 2
Module Credits: 5.00
Academic Year: 2021/22
Semester: SEM1
Originating Department: Shool of AI and Advanced Computing
Pre-requisites: N/A
To provide an understanding of

1) the design and construction of a pattern recognition system

2) the major approaches in statistical and syntactic pattern recognition.

The student should also have some exposure to the theoretical issues involved in pattern recognition system design such as the curse of dimensionality.

Finally, the student will have a clear working knowledge of implementing pattern recognition techniques and the scientific Python computing environment.

These goals are evaluated through the course project, coursework, and exams.
Learning outcomes 
A. Understand foundations of pattern recognition algorithms and machines, including statistical, structural and neural methods;

B . Understand data structures for pattern representation, feature discovery and selection;

C. Carry out classification vs. description, parametric and nonparametric classification, supervised and unsupervised learning;

D. Use of contextual evidence, clustering, recognition with strings, and small sample-size problems.
Method of teaching and learning 
This module will be delivered by a combination of formal lectures and tutorials.
Introduction to pattern recognition (1 lectures)

Decision Theory – generalization (1 lecture)

Dimensionality reduction - Low-Dimensional Representations and Multidimensional Scaling (4 lectues)

Classification using distance metrics (1 lecture)

Classification using density functions – normal density functions with different structures of covariance matrices (4 lectures)

Classification using Bayesian decision theory – continuous features, two-category classification, minimum-error rate classification, classifiers, discriminant functions and surfaces, discriminant functions for normal density, error bounds for normal densities, discrete features, missing and noisy features (8 lectures)

Classification using Multilayer Perceptions – feedforward operation and classification, backpropagation algorithm, backpropagation and bayes theorem, and practical techniques for improving backpropagation (8 lectures)

Validation – cross validation, and techniques (2 lectures)

Unsupervised Learning – mixture densities and identifiability, maximum likelihood estimates, application to normal mixtures, unsupervised Bayesian learning, data description and clustering, critrion for clustering, hierarchical clustering (8 lectures)

Feature Selection (2 lectures)
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 39    13      98  150 


Sequence Method % of Final Mark
1 Coursework (Groupwork) 25.00
2 Final Exam 50.00
3 Project 25.00

Module Catalogue generated from SITS CUT-OFF: 6/5/2020 5:36:13 PM