Course Outline
Lesson One: MATLAB Fundamentals
1. Overview of MATLAB installation, version history, and programming environment
2. Basic MATLAB operations (including matrix manipulation, logical and flow control, functions, script files, and basic plotting)
3. Data import (formats such as mat, txt, xls, csv)
Lesson Two: Advanced MATLAB Techniques
1. MATLAB programming habits and coding style
2. Debugging techniques in MATLAB
3. Vectorized programming and memory optimization
4. Graphics objects and handles
Lesson Three: Backpropagation Neural Networks
1. Basic principles of backpropagation neural networks
2. Implementation of backpropagation neural networks in MATLAB
3. Practical case studies
4. Optimization of parameters for backpropagation neural networks
Lesson Four: RBF, GRNN, and PNN Neural Networks
1. Basic principles of Radial Basis Function (RBF) neural networks
2. Basic principles of Generalized Regression Neural Networks (GRNN)
3. Basic principles of Probabilistic Neural Networks (PNN)
4. Practical case studies
Lesson Five: Competitive Neural Networks and Self-Organizing Maps (SOM)
1. Basic principles of competitive neural networks
2. Basic principles of Self-Organizing Maps (SOM)
3. Practical case studies
Lesson Six: Support Vector Machines (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression modeling
3. Common training algorithms for SVM (batch processing, SMO, incremental learning)
4. Practical case studies
Lesson Seven: Extreme Learning Machine (ELM)
1. Basic principles of ELM
2. Differences and relationships between ELM and backpropagation neural networks
3. Practical case studies
Lesson Eight: Decision Trees and Random Forests
1. Basic principles of decision trees
2. Basic principles of random forests
3. Practical case studies
Lesson Nine: Genetic Algorithms (GA)
1. Basic principles of genetic algorithms
2. Introduction to commonly used genetic algorithm toolboxes
3. Practical case studies
Lesson Ten: Particle Swarm Optimization (PSO) Algorithm
1. Basic principles of the particle swarm optimization algorithm
2. Practical case studies
Lesson Eleven: Ant Colony Algorithm (ACA)
1. Basic principles of the ant colony optimization algorithm
2. Practical case studies
Lesson Twelve: Simulated Annealing (SA) Algorithm
1. Basic principles of the simulated annealing algorithm
2. Practical 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 (optimization search, Filter, and Wrapper approaches)
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
