Course Outline
Lesson 1: Introduction to MATLAB Basics
1. Brief overview of MATLAB installation, version history, and the programming environment
2. Fundamental MATLAB operations (including matrix manipulations, logic and flow control, functions and script files, basic plotting, etc.)
3. File import (formats such as .mat, .txt, .xls, .csv, etc.)
Lesson 2: Advanced MATLAB and Improvement
1. MATLAB coding habits and style
2. MATLAB debugging techniques
3. Vectorized programming and memory optimization
4. Graphics objects and handles
Lesson 3: BP Neural Networks
1. Fundamental principles of BP neural networks
2. MATLAB implementation of BP neural networks
3. Case studies and practical examples
4. Optimization of BP neural network parameters
Lesson 4: RBF, GRNN, and PNN Neural Networks
1. Fundamental principles of RBF neural networks
2. Fundamental principles of GRNN neural networks
3. Fundamental principles of PNN neural networks
4. Case studies and practical examples
Lesson 5: Competitive Neural Networks and SOM Neural Networks
1. Fundamental principles of competitive neural networks
2. Fundamental principles of Self-Organizing Map (SOM) neural networks
3. Case studies and practical examples
Lesson 6: Support Vector Machine (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. Case studies and practical examples
Lesson 7: Extreme Learning Machine (ELM)
1. Fundamental principles of ELM
2. Differences and connections between ELM and BP neural networks
3. Case studies and practical examples
Lesson 8: Decision Trees and Random Forests
1. Fundamental principles of decision trees
2. Fundamental principles of random forests
3. Case studies and practical examples
Lesson 9: Genetic Algorithm (GA)
1. Fundamental principles of genetic algorithms
2. Overview of common genetic algorithm toolboxes
3. Case studies and practical examples
Lesson 10: Particle Swarm Optimization (PSO) Algorithm
1. Fundamental principles of particle swarm optimization algorithms
2. Case studies and practical examples
Lesson 11: Ant Colony Algorithm (ACA)
1. Fundamental principles of particle swarm optimization algorithms
2. Case studies and practical examples
Lesson 12: Simulated Annealing (SA) Algorithm
1. Fundamental principles of simulated annealing algorithms
2. Case studies and practical examples
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 (optimization search, Filter, and Wrapper, 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
