Course Outline
Lesson 1: MATLAB Basics and Fundamentals
1. Overview of MATLAB installation, version history, and programming environment
2. MATLAB basic operations (including matrix operations, logic and flow control, functions and script files, and basic plotting)
3. Importing files (formats such as mat, txt, xls, csv, etc.)
Lesson 2: MATLAB Advanced Techniques
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. 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. 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. Case studies
Lesson 6: Support Vector Machines (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression and fitting
3. Common training algorithms for SVM (blocking, SMO, incremental learning, etc.)
4. Case studies
Lesson 7: Extreme Learning Machine (ELM)
1. Basic principles of ELM
2. Differences and relationships between ELM and BP neural networks
3. Case studies
Lesson 8: Decision Trees and Random Forests
1. Basic principles of decision trees
2. Basic principles of random forests
3. Case studies
Lesson 9: Genetic Algorithm (GA)
1. Basic principles of the genetic algorithm
2. Introduction to common genetic algorithm toolboxes
3. Case studies
Lesson 10: Particle Swarm Optimization (PSO) Algorithm
1. Basic principles of the particle swarm optimization algorithm
2. Case studies
Lesson 11: Ant Colony Algorithm (ACA)
1. Basic principles of the particle swarm optimization algorithm
2. Case studies
Lesson 12: Simulated Annealing Algorithm (SA)
1. Basic principles of the simulated annealing algorithm
2. 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
