Artificial Intelligence and Machine Learning using MATLAB

This comprehensive course is designed for engineers looking to master MATLAB’s capabilities for AI-driven applications. From mastering the fundamentals of MATLAB programming to exploring advanced matrix operations and data analysis techniques, this course offers a structured pathway to understanding AI concepts. Participants will gain hands-on experience with machine learning algorithms, neural networks, and reinforcement learning, all tailored for practical engineering and industrial applications.

Foundations of MATLAB

3 Days

• Navigating the MATLAB interface and understanding core functionalities
• Basics of variables, arrays, operators, and fundamental functions
• Importing and managing data from various file formats
• Creating and customizing data visualizations
• Exporting graphics and data for reports

MATLAB Programming Essentials

2 Days

• Introduction to control structures: Loops, if-else statements, and error handling
• Writing and utilizing functions effectively

Matrix Operations

2 Days

• Creating and manipulating matrices
• Performing mathematical operations with matrices
• Applications of matrices: Transformations, rotations, and solving linear equations
• Introduction to least-square methods

Data Analysis and Processing

3 Days

• Advanced data types: Structure arrays, cell arrays, categorical data, and datetime objects
• Organizing and analyzing tabular data
• Conditional data selection and filtering
• Importing and exporting datasets: .mat files, text data, and tabular formats

AI with Matlab

7 Days

• Overview of core AI concepts and methodologies
• Data Engineering: Feature extraction and data preprocessing
• Introduction to machine learning algorithms (focus on classification and regression)
• Introduction to neural networks
• Introduction to reinforcement learning
• Practical applications of AI in engineering and industry
• Hands-on exercises

Capstone Project

2 Days

• Application of course concepts on a real-world project
• Presentation and peer review of project results