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Course Outline
Lesson 1: MATLAB Basics
1. Briefly introduce the installation, version history and programming environment of MATLAB
2. MATLAB Basic operations (including matrix operations, logic and process control, functions and script files, basic drawing, etc.)
3. File import (mat, txt, xls, csv, etc.)
Lesson 2: MATLAB Advancement and Improvement
1. MATLAB Programming habits and style
2. MATLAB Debugging tips
3. Vectorized programming and memory optimization
4. Graphics objects and handles
Lesson 3: BP Neural Network
1. The basic principle of BP neural network
2. Implementation of BP neural networkMATLAB
3. Case practice
4. Optimization of BP neural network parameters
Lesson 4: RBF, GRNN and PNN Neural Networks
1. Basic principles of RBF neural network
2. Basic principles of GRNN neural network
3. Basic principles of PNN neural network
4. Case Practice
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 network
3. Case practice
Lesson 6: Support Vector Machine (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression fitting
3. Common training algorithms for SVM (blocking, SMO, incremental learning, etc.)
4. Case Practice
Lesson 7: Extreme Learning Machine (ELM)
1. Basic principles of ELM
2. The difference and connection between ELM and BP neural network
3. Case practice
Lesson 8: Decision Trees and Random Forests
1. The basic principle of decision tree
2. Basic principles of random forest
3. Case practice
Lesson 9: Genetic Algorithm (GA)
1. Basic principles of genetic algorithms
2. Introduction to Common Genetic Algorithm Toolbox
3. Case practice
Lesson 10: Particle Swarm Optimization (PSO) Algorithm
1. Basic principles of particle swarm optimization algorithm
2. Case practice
Lesson 11: Ant Colony Algorithm (ACA)
1. Basic principles of particle swarm optimization algorithm
2. Case practice
Lesson 12: Simulated Annealing (SA)
1. Basic principles of simulated annealing algorithm
2. Case practice
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 (optimized search, filter and wrapper, etc.)
Requirements
Advanced Mathematics Linear Algebra
21 Hours
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
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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.
