Course Outline
Lesson One: MATLAB Basics
1. Overview of MATLAB installation, version history, and programming environment
2. MATLAB fundamentals (including matrix operations, logic and flow control, functions and script files, basic plotting, etc.)
3. File import (formats such as mat, txt, xls, csv, etc.)
Lesson Two: Advanced MATLAB and Enhancement
1. MATLAB programming habits and style
2. MATLAB debugging techniques
3. Vectorized programming and memory optimization
4. Graphics objects and handles
Lesson Three: 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 SOM Neural Networks
1. Basic principles of competitive neural networks
2. Basic principles of Self-Organizing Feature Map (SOM) neural networks
3. Case studies
Lesson Six: Support Vector Machine (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression fitting
3. Common SVM training algorithms (batch processing, SMO, incremental learning, etc.)
4. Case studies
Lesson Seven: Extreme Learning Machine (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 Algorithm (GA)
1. Basic principles of genetic algorithms
2. Overview of common genetic algorithm toolboxes
3. Case studies
Lesson Ten: Particle Swarm Optimization (PSO) Algorithm
1. Basic principles of the particle swarm optimization algorithm
2. Case studies
Lesson Eleven: Ant Colony Algorithm (ACA)
1. Basic principles of the particle swarm optimization 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
2. Basic principles of Partial Least Squares
3. Common feature selection methods (optimization 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
