Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: EEE418
Module Title: Advanced Pattern Recognition
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM2
Originating Department: Electrical and Electronic Engineering
Pre-requisites: N/A
   
Aims
To introduce the techniques and concept of pattern recognition.

To develop the necessary skills to carry out and report simple experiments of pattern recognition
Learning outcomes 
A Understand recent advances of pattern recognition.

B Develop various techniques used in pattern recognition.

C Design pattern recognition systems to achieve the specified objectives e.g., on performance and cost.

D Formulate and define practical problems and to use pattern recognition methodologies to analyze and solve engineering problems.

Method of teaching and learning 
This module will be delivered through a combination of formal lectures, supervised laboratory sessions and seminars. There are 3 lab reports that will be assigned across the semester.
Syllabus 
1- Introduction

History of pattern recognition;

Example of typical pattern recognition system ;

Overview of techniques in pattern recognition ;


2 Statistical Pattern Recognition

Bayesian decision theory, Parametric /non- Parametric, Gaussian Classifiers, Maximum Likelihood Estimation, Expectation-Maximisation (EM), Clustering, Loss function, Regularization;

3 Feature Extraction and Selection

Linear Discriminant Analysis (LDA);

Primary Component Analysis (PCA);

Forward feature selection;

Typical Features, e.g., Scale-invariant feature transform (SIFT);


4 Statistical Classifiers

Linear Classifiers, naïve Bayes classifier;

Support Vector Machine (SVM);

Neural Network;



5 Ensemble Learning

Tree classifier

Random Forest

Bagging

Boosting


6 Structural Patten Recognition

Syntactic parsing,

String matching,

Graph matching,

Hidden Markov Model (HMM),

Markov random field (MRF), Conditional Random Field (CRF);


7 Deep Learning

Basic introduction,

Auto-Encode/Decode,

Convolutional neural network;

Recurrent neural network;



8 Applications

Face recognition;

Handwriting recognition;

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26  4  8  12       

Assessment

Sequence Method % of Final Mark
1 Lab Report 1 10.00
2 Lab Report 2 10.00
3 Lab Report 3 10.00
4 Final Exam 70.00

Module Catalogue generated from SITS CUT-OFF: 8/20/2019 6:19:05 PM