Course Outline
Lesson One: MATLAB Fundamentals
1. An introduction to MATLAB's installation, version history, and programming environment
2. MATLAB basic operations (including matrix operations, logic and flow control, functions and script files, and basic plotting)
3. File import (formats such as mat, txt, xls, csv, etc.)
Lesson Two: Advanced MATLAB Techniques
1. MATLAB programming habits and style
2. MATLAB debugging techniques
3. Vectorised programming and memory optimization
4. Graphics objects and handles
Lesson Three: Backpropagation (BP) Neural Networks
1. Basic principles of BP neural networks
2. Implementation of BP neural networks in MATLAB
3. Case studies
4. Optimization of BP neural network parameters
Lesson Four: RBF, GRNN, and PNN Neural Networks
1. Basic principles of RBF neural networks
2. Basic principles of GRNN neural networks
3. Basic principles of PNN neural networks
4. Case studies
Lesson Five: Competitive Neural Networks and Self-Organising Maps (SOM)
1. Basic principles of competitive neural networks
2. Basic principles of Self-Organising Feature Maps (SOM) neural networks
3. Case studies
Lesson Six: Support Vector Machines (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression and fitting
3. Common SVM training algorithms (chunking, SMO, incremental learning, etc.)
4. Case studies
Lesson Seven: Extreme Learning Machines (ELM)
1. Basic principles of ELM
2. Differences and connections between ELM and BP neural networks
3. Case studies
Lesson Eight: Decision Trees and Random Forests
1. Basic principles of decision trees
2. Basic principles of random forests
3. Case studies
Lesson Nine: Genetic Algorithms (GA)
1. Basic principles of genetic algorithms
2. Introduction to common genetic algorithm toolboxes
3. Case studies
Lesson Ten: Particle Swarm Optimisation (PSO) Algorithm
1. Basic principles of the Particle Swarm Optimisation algorithm
2. Case studies
Lesson Eleven: Ant Colony Algorithm (ACA)
1. Basic principles of the Ant Colony Optimisation algorithm
2. Case studies
Lesson Twelve: Simulated Annealing (SA) Algorithm
1. Basic principles of the Simulated Annealing algorithm
2. Case studies
Lesson Thirteen: Dimensionality Reduction and Feature Selection
1. Basic principles of Principal Component Analysis (PCA)
2. Basic principles of Partial Least Squares (PLS)
3. Common feature selection methods (optimisation search, Filter, Wrapper, etc.)
Requirements
Higher 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
