Course Outline
Lesson One: MATLAB Fundamentals
1. An 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. File import (formats such as mat, txt, xls, csv, etc.)
Lesson Two: MATLAB Advanced Techniques and Improvement
1. MATLAB programming habits and style
2. MATLAB debugging techniques
3. Vectorised programming and memory optimisation
4. Graphics objects and handles
Lesson Three: Back Propagation Neural Networks
1. Basic principles of Back Propagation (BP) neural networks
2. Implementation of BP neural networks in MATLAB
3. Case study
4. Optimisation of BP neural network parameters
Lesson Four: RBF, GRNN and PNN Neural Networks
1. Basic principles of Radial Basis Function (RBF) neural networks
2. Basic principles of Generalised Regression Neural Network (GRNN)
3. Basic principles of Probabilistic Neural Network (PNN)
4. Case study
Lesson Five: Competitive Neural Networks and SOM
1. Basic principles of competitive neural networks
2. Basic principles of Self-Organising Feature Map (SOM) neural networks
3. Case study
Lesson Six: Support Vector Machine (SVM)
1. Basic principles of SVM classification
2. Basic principles of SVM regression fitting
3. Common SVM training algorithms (including Batching, SMO, and Incremental Learning)
4. Case study
Lesson Seven: Extreme Learning Machine (ELM)
1. Basic principles of ELM
2. Differences and connections between ELM and BP neural networks
3. Case study
Lesson Eight: Decision Trees and Random Forests
1. Basic principles of decision trees
2. Basic principles of random forests
3. Case study
Lesson Nine: Genetic Algorithm (GA)
1. Basic principles of genetic algorithms
2. Introduction to common genetic algorithm toolboxes
3. Case study
Lesson Ten: Particle Swarm Optimisation (PSO) Algorithm
1. Basic principles of the PSO algorithm
2. Case study
Lesson Eleven: Ant Colony Algorithm (ACA)
1. Basic principles of the PSO algorithm
2. Case study
Lesson Twelve: Simulated Annealing (SA) Algorithm
1. Basic principles of the Simulated Annealing algorithm
2. Case study
Lesson Thirteen: Dimensionality Reduction and Feature Selection
1. Basic principles of Principal Component Analysis
2. Basic principles of Partial Least Squares
3. Common feature selection methods (including optimisation search, Filter, and Wrapper, etc.)
Requirements
Higher Mathematics
Linear Algebra
Testimonials (2)
Availability and adaptability, responses to questions
Jean-Michel MEOT - CIRAD
Course - Introduction au Machine Learning avec MATLAB
Machine Translated
Gradual introduction and application of methods
Aurelien Briffaz - CIRAD
Course - Introduction au Machine Learning avec MATLAB
Machine Translated
