Course Outline
Lesson 1: MATLAB Basics
1. Brief introduction to MATLAB installation, version history, and programming environment
2. MATLAB basic operations (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 2: MATLAB Advanced Skills 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: BP Neural Networks
1. Basic principles of BP neural networks
2. Implementation of BP neural networks in MATLAB
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 Feature Map (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 modeling
3. Common training algorithms for SVM (block-wise, 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 PSO algorithm
2. Practical case studies
Lesson 11: Ant Colony Algorithm (ACA)
1. Basic principles of the ant colony optimization algorithm
2. Practical case studies
Lesson 12: Simulated Annealing (SA) Algorithm
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 (PCA)
2. Basic principles of Partial Least Squares (PLS)
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
