Course Outline
Lesson 1: MATLAB Basics
1. Overview of MATLAB installation, version history, and programming environment
2. Basic MATLAB operations (including matrix manipulation, logic and flow control, functions and script files, basic plotting, etc.)
3. Data import (formats such as mat, txt, xls, csv, etc.)
Lesson 2: 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 3: Backpropagation (BP) Neural Networks
1. Basic principles of BP neural networks
2. MATLAB implementation of BP neural networks
3. Practical case studies
4. Optimization 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. Practical case studies
Lesson 5: Competitive Neural Networks and SOM Neural Networks
1. Basic principles of competitive neural networks
2. Basic principles of Self-Organizing Maps (SOM) neural networks
3. Practical 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 (blockwise, SMO, incremental learning, etc.)
4. Practical case studies
Lesson 7: Extreme Learning Machines (ELM)
1. Basic principles of ELM
2. Differences and relationships between ELM and BP neural networks
3. Practical case studies
Lesson 8: Decision Trees and Random Forests
1. Basic principles of decision trees
2. Basic principles of random forests
3. Practical case studies
Lesson 9: Genetic Algorithms (GA)
1. Basic principles of genetic algorithms
2. Introduction to common genetic algorithm toolboxes
3. Practical case studies
Lesson 10: Particle Swarm Optimization (PSO) Algorithm
1. Basic principles of the Particle Swarm Optimization algorithm
2. Practical case studies
Lesson 11: Ant Colony Algorithm (ACA)
1. Basic principles of the Particle Swarm Optimization algorithm
2. Practical case studies
Lesson 12: Simulated Annealing Algorithm (SA)
1. Basic principles of the Simulated Annealing algorithm
2. Practical 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 (optimization search, Filter, 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
