Course Outline
Lesson 1: MATLAB Basics for Beginners
1. Brief overview of MATLAB 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 (mat, txt, xls, csv formats, etc.)
Lesson 2: MATLAB Advanced Features and Improvements
1. MATLAB programming habits and style
2. MATLAB debugging techniques
3. Vectorised programming and memory optimisation
4. Graphics objects and handles
Lesson 3: BP Neural Networks
1. Basic principles of BP neural networks
2. MATLAB implementation of BP neural networks
3. Case studies
4. Optimisation of BP neural network parameters
Lesson 4: 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 5: Competitive Neural Networks and SOM Neural Networks
1. Basic principles of competitive neural networks
2. Basic principles of Self-Organising Feature Map (SOM) neural networks
3. Case studies
Lesson 6: Support Vector Machines (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression fitting
3. Common SVM training algorithms (partitioning, SMO, incremental learning, etc.)
4. Case studies
Lesson 7: Extreme Learning Machines (ELM)
1. Basic principles of ELM
2. Differences and connections between ELM and BP neural networks
3. Case studies
Lesson 8: Decision Trees and Random Forests
1. Basic principles of decision trees
2. Basic principles of random forests
3. Case studies
Lesson 9: Genetic Algorithm (GA)
1. Basic principles of genetic algorithms
2. Introduction to common genetic algorithm toolboxes
3. Case studies
Lesson 10: Particle Swarm Optimisation (PSO) Algorithm
1. Basic principles of the particle swarm optimisation algorithm
2. Case studies
Lesson 11: Ant Colony Algorithm (ACA)
1. Basic principles of the particle swarm optimisation algorithm
2. Case studies
Lesson 12: Simulated Annealing (SA) Algorithm
1. Basic principles of the simulated annealing algorithm
2. Case studies
Lesson 13: Dimensionality Reduction and Feature Selection
1. Basic principles of Principal Component Analysis
2. Basic principles of Partial Least Squares
3. Common feature selection methods (optimisation search, Filter, Wrapper, etc.)
Requirements
Higher Mathematics
Linear Algebra
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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
