Course Outline
Lesson 1: Getting Started with MATLAB
1. Brief introduction to MATLAB installation, version history, and the programming environment
2. Basic MATLAB operations (including matrix operations, logic and flow control, functions and script files, and fundamental plotting)
3. File import (formats such as .mat, .txt, .xls, .csv, etc.)
Lesson 2: Advanced MATLAB and Performance Enhancement
1. MATLAB coding conventions and style
2. MATLAB debugging techniques
3. Vectorised programming and memory optimisation
4. Graphics objects and handles
Lesson 3: Backpropagation (BP) Neural Networks
1. Fundamental principles of BP neural networks
2. Implementation of BP neural networks in MATLAB
3. Practical case studies
4. Optimisation of BP neural network parameters
Lesson 4: RBF, GRNN, and PNN Neural Networks
1. Fundamental principles of Radial Basis Function (RBF) neural networks
2. Fundamental principles of Generalised Regression Neural Networks (GRNN)
3. Fundamental principles of Probabilistic Neural Networks (PNN)
4. Practical case studies
Lesson 5: Competitive Neural Networks and Self-Organising Map (SOM) Networks
1. Fundamental principles of competitive neural networks
2. Fundamental principles of Self-Organising Map (SOM) neural networks
3. Practical case studies
Lesson 6: Support Vector Machines (SVM)
1. Fundamental principles of SVM classification
2. Fundamental principles of SVM regression fitting
3. Common SVM training algorithms (chunking, SMO, incremental learning, etc.)
4. Practical case studies
Lesson 7: Extreme Learning Machines (ELM)
1. Fundamental principles of ELM
2. Differences and connections between ELM and BP neural networks
3. Practical case studies
Lesson 8: Decision Trees and Random Forests
1. Fundamental principles of decision trees
2. Fundamental principles of random forests
3. Practical case studies
Lesson 9: Genetic Algorithms (GA)
1. Fundamental principles of genetic algorithms
2. Overview of common genetic algorithm toolboxes
3. Practical case studies
Lesson 10: Particle Swarm Optimisation (PSO) Algorithms
1. Fundamental principles of particle swarm optimisation algorithms
2. Practical case studies
Lesson 11: Ant Colony Algorithms (ACA)
1. Fundamental principles of ant colony algorithms
2. Practical case studies
Lesson 12: Simulated Annealing (SA) Algorithms
1. Fundamental principles of simulated annealing algorithms
2. Practical case studies
Lesson 13: Dimensionality Reduction and Feature Selection
1. Fundamental principles of Principal Component Analysis (PCA)
2. Fundamental principles of Partial Least Squares (PLS)
3. Common feature selection methods (optimised search, Filter, and Wrapper approaches, etc.)
Requirements
Advanced Mathematics
Linear Algebra
Testimonials (2)
The many examples and the building of the code from start to finish.
Toon - Draka Comteq Fibre B.V.
Course - Introduction to Image Processing using Matlab
Many useful exercises, well explained
