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Course Outline
Lesson 1: Introduction to MATLAB Basics
1. A brief introduction to MATLAB installation, version history, and programming environment
2. Basic operations in MATLAB (including matrix operations, logical and flow control, functions and script files, basic plotting, etc.)
3. File import (mat, txt, xls, csv formats)
Lesson 2: Advanced MATLAB Programming
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 Network
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 maps (SOM) neural networks
3. Case studies
Lesson 6: Support Vector Machine (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression fitting
3. Common training algorithms for SVMs (chunking, SMO, incremental learning, etc.)
4. Case studies
Lesson 7: Extreme Learning Machine (ELM)
1. Basic principles of ELM
2. Differences and connections 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 genetic algorithms
2. Introduction to common genetic algorithm toolboxes
3. Case studies
Lesson 10: Particle Swarm Optimization (PSO) Algorithm
1. Basic principles of particle swarm optimization algorithm
2. Case studies
Lesson 11: Ant Colony Algorithm (ACA)
1. Basic principles of ant colony algorithm
2. Case studies
Lesson 12: Simulated Annealing (SA) Algorithm
1. Basic principles of 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
21 Hours
Testimonials (2)
Hands on building of the code from scratch.
Igor - Draka Comteq Fibre B.V.
Course - Introduction to Image Processing using Matlab
Trainer took the initiative to cover additional content outside our course materials to improve our learning.
