Basic MATLAB Programming Training Course

Module 1: MATLAB Environment, Workflows, and Data Foundation

Establishes mastery of the MATLAB development ecosystem, covering both desktop and cloud-based workflows, core data types, file input/output (I/O), and data management strategies that serve as the foundation for advanced technical computing tasks.

1.1 The MATLAB Ecosystem: Desktop, Online, and Drive

• Navigating the MATLAB desktop environment: Command Window, Editor, Workspace, Current Folder, and Command History
• MATLAB Online: cloud-based development, collaboration via MATLAB Drive, and cross-device access
• Managing the workspace, search paths, and environment configuration
• Using shortcuts, profiles, and customizing the environment for engineering efficiency

1.2 Core Data Types and Mathematical Foundations

• Understanding literals, variables, naming conventions, and assignment in MATLAB
• Creating, indexing, and manipulating scalars, vectors, matrices, and multidimensional arrays
• Utilizing constants, operators, and built-in mathematical functions
• Distinguishing between array operations and matrix operations: element-wise versus linear algebra
• Applying logical indexing, relational operators, and logical arrays for advanced filtering
• Using cell arrays, structures, structs, and handle objects for complex data organization
• Leveraging tables and timetables: MATLAB's modern approach to tabular data for time-series and experimental datasets

1.3 File I/O and Data Interoperability

• Importing and exporting CSV, TXT, and delimited text files
• Interacting with Excel spreadsheets: performing read, write, and formatting operations
• Understanding MAT native file formats (.mat) and workspace persistence
• Using the Import Wizard and generating automated data import code
• Connecting to databases: linking to SQL Server, Oracle, PostgreSQL, and cloud databases
• Handling web data: retrieving JSON, XML, and REST API responses within MATLAB

Market-Aligned Competencies: MATLAB Development Environment, MATLAB Online Workflow, MATLAB Drive Collaboration, Numerical Data Management, Scientific Computing Fundamentals, Technical Data Import and Export, CSV and Excel Data Handling, Database Connectivity, MATLAB Tables and Timetables, Structured Data Organization, Mathematical Computing Basics, Engineering Data Workflows

Module 2: MATLAB Programming, Algorithms, and Code Architecture

Advances programming proficiency beyond basic syntax, covering structured programming, object-oriented MATLAB, code organization, debugging, performance profiling, and software engineering best practices for maintaining robust technical codebases.

2.1 Structured Programming and Control Flow

• Scripts versus functions: guidelines for when to use each and best practices
• Conditional logic: implementing if/else, switch/case, and nested conditions
• Loop structures: for, while, and strategies for loop optimization (vectorization vs. iteration)
• Managing control flow within subfunctions and nested functions
• Error handling and debugging techniques: utilizing try/catch, assert, dbstop, and the MATLAB Debugger

2.2 Function Programming and Code Organization

• Creating functions, managing input/output arguments, and leveraging varargin/varargout for flexibility
• Utilizing anonymous functions and function handles for functional programming in MATLAB
• Working with subfunctions, local functions, and nested functions
• Organizing code via files, packages, and folder-level management
• Understanding pass-by-value versus pass-by-reference (handle objects)

2.3 Object-Oriented Programming in MATLAB

• Defining classes: properties, methods, and access levels (public, private, protected)
• Distinguishing between handle classes and value classes: value semantics versus reference semantics
• Managing object lifecycles: constructors, destructors, and lifecycle management
• Implementing inheritance, method overriding, and abstract classes
• Creating interfaces and handling events in MATLAB classes
• Utilizing static methods, dynamic properties, and property validation

2.4 Profiling, Code Quality, and Testing

• Using the MATLAB profiler to identify bottlenecks and optimize compute-intensive code
• Analyzing code coverage and using the MTest unit testing framework
• Integrating version control: implementing Git and SVN workflows in the MATLAB Editor
• Understanding Continuous Integration (CI/CD) concepts with Jenkins and MATLAB CI Pipelines
• Addressing static code analysis warnings and adhering to best practices

Market-Aligned Competencies: MATLAB Programming and Scripting, Algorithm Development and Optimization, Object-Oriented MATLAB Programming, Function-Based Architecture, Vectorization and Performance Optimization, MATLAB Debugging and Error Handling, Code Profiling and Performance Tuning, MATLAB Unit Testing (MTest), Code Coverage Analysis, Version Control with Git, Continuous Integration (CI/CD), Professional Code Quality Standards, Software Engineering for Technical Computing

Module 3: Data Visualization, Reporting, and Interactive Apps

Covers plotting fundamentals through advanced visualization, interactive dashboard creation, GUI development with App Designer, live scripting for reproducible reports, and automated report generation for engineering documentation.

3.1 Fundamental and Advanced Plotting

• 2D plotting: line plots, scatter plots, bar charts, pie charts, area plots, and error bars
• Multi-axis plotting: using hold, subplot, tiledlayout, and axes positioning
• 3D plotting: surf, mesh, contour, slice, and volume visualization
• Customizing plots: titles, labels, legends, annotations, line styles, markers, and colors
• Utilizing colormaps, colorbars, and ensuring perceptually accurate plots
• Exporting high-resolution figures for publications in formats such as PNG, PDF, SVG, and EMF

3.2 Interactive Visualization and Dashboards

• Customizing figures with UI controls: sliders, buttons, dropdowns, and callbacks
• Using MATLAB App Designer to build interactive desktop applications with drag-and-drop UI components
• Managing plot interactions: zoom, pan, brushing, and selection callbacks
• Creating web apps: deploying MATLAB visualizations as online interactive dashboards

3.3 Live Scripts and Automated Reporting

• MATLAB Live Script (.mlx): executable notebooks combining code, plots, and formatted text
• Supporting Markdown and LaTeX in Live Scripts for mathematical equations
• Creating custom Live Script sections, input parameters, and sharing workflows
• Automating report generation: exporting Live Scripts to PDF, HTML, and Word formats

Market-Aligned Competencies: Data Visualization and Plotting, MATLAB App Designer, GUI Development, Interactive Dashboard Design, Live Script Authoring, Technical Report Generation, Scientific Data Presentation, 3D Visualization and Plotting, MATLAB Graphics System, Engineering Visualization, Publication-Quality Figure Design, Web App Deployment, Interactive Scientific Computing

Module 4: Matrix Algebra, Linear Optimization, and Symbolic Mathematics

Provides comprehensive coverage of linear algebra as the mathematical core of MATLAB, linear programming optimization, and symbolic computation for analytical solutions. This is essential for engineering, operations research, and scientific modeling applications.

4.1 Linear Algebra and Matrix Operations

• Matrix construction: using eye, zeros, ones, rand, randn, diag, and special matrices
• Matrix decomposition: LU, QR, Cholesky, SVD, and eigenvalue analysis
• Special functions: det, trace, rank, norm, condition number, and pseudo-inverse
• Solving linear systems: using left division (\), mldivide, and least squares solutions
• Analyzing eigenvalues and eigenvectors, and applying matrix functions (expm, logm, sqrtm)
• Performing sparse matrix operations for memory-efficient computing

4.2 Optimization Fundamentals

• Linear programming: using linprog for constrained optimization
• Nonlinear optimization: utilizing fmincon, fminsearch, and fzero
• Curve fitting and parameter estimation: using fit, polyfit, and lsqcurvefit
• Introduction to the Optimization Toolbox workflow

4.3 Symbolic Mathematics

• Creating symbolic variables and manipulating symbolic expressions
• Performing analytical differentiation and integration using dsolve and int
• Utilizing variable-precision arithmetic (vpa) for high-precision computation
• Applying Laplace and Fourier transforms in symbolic mode
• Solving equations analytically: using solve and vpasolve

Market-Aligned Competencies: Linear Algebra and Matrix Computations, Matrix Decomposition and Analysis, Optimization and Mathematical Programming, Linear Programming, Nonlinear Optimization, Curve Fitting and Data Approximation, Symbolic Mathematics and Analytical Computing, Laplace Transforms, Eigenvalue Analysis and Numerical Stability, Sparse Matrix Computation, Scientific Computing and Numerical Analysis

Module 5: Signal Processing, Image Processing, and Simulation

Applies MATLAB's industry-standard toolboxes to signal analysis, image processing, and system simulation. This module covers the core toolboxes most in-demand in telecommunications, audio processing, biomedical engineering, and industrial inspection sectors.

5.1 Signal Processing Fundamentals

• Sampling theory: sampling rate, aliasing, and the Nyquist criterion
• Fundamental signal generation: sine, cosine, square, sawtooth, and chirp signals
• li>Fundamental signal generation: sine, cosine, square, sawtooth, and chirp signals
• Frequency domain analysis: FFT, spectrogram, and magnitude/phase plots
• Filter design: lowpass, highpass, bandpass, bandstop FIR and IIR filters
• Spectral analysis, power spectral density, and filtering applications
• Signal denoising, smoothing, and envelope detection

5.2 Image and Video Processing

• Creating, reading, writing, and displaying images with the MATLAB Image Processing Toolbox
• Image enhancement: contrast adjustment, histogram equalization, and filtering
• Image segmentation: thresholding, edge detection, and watershed methods
• Geometric transformations and image registration
• Morphological operations: dilation, erosion, opening, and closing
• Feature detection: corner detection (Harris), blob detection, and template matching

5.3 Introduction to Simulink and System Modeling

• Navigating the Simulink environment: model creation, blocks library, and signal routing
• Building block diagrams: sources, sinks, continuous/discrete blocks, and integrators
• Setting simulation parameters: solver selection, step size, and simulation duration
• Using subsystems, masks, and library blocks for reusable components
• Model analysis: using scopes, diagnostic messages, and the Model Explorer
• Introduction to Simulink for control systems: plant modeling and controller simulation

5.4 Control Systems and Dynamical Systems

• Understanding transfer functions and block diagrams in the Control System Toolbox
• Performing step, impulse, frequency (Bode), and root locus analysis
• Fundamentals of PID controller design and tuning
• State-space representation and system analysis

Market-Aligned Competencies: Digital Signal Processing (DSP), FFT Analysis and Filtering, Image Processing and Computer Vision, MATLAB Image Processing Toolbox, Image Segmentation and Feature Detection, Simulink Model-Based Design, Control Systems Engineering, Transfer Function Analysis, PID Controller Design, Dynamical System Simulation, Spectral Analysis, Bode Plot and Frequency Response, Root Locus Analysis, State-Space Modeling, Biomedical Signal Processing, Audio Signal Processing, Industrial Inspection and Quality Control

Module 6: Machine Learning, Deep Learning, and AI Integration

Covers the rapidly expanding AI/ML capabilities within MATLAB, from classical supervised and unsupervised learning to deep neural networks, pre-trained models, and integration with Python for hybrid AI workflows. This addresses the most in-demand technical skill sets in engineering today.

6.1 Classical Machine Learning with MATLAB

• Classification algorithms: KNN, Naive Bayes, SVM, decision trees, and ensemble methods
• Regression algorithms: linear regression, polynomial regression, and regularized regression
• Unsupervised learning: clustering (k-means, hierarchical), PCA, and dimensionality reduction
• Model validation: cross-validation, confusion matrices, ROC curves, and accuracy metrics
• Feature selection, data preprocessing, and train/validation/test splitting

6.2 Deep Learning in MATLAB

• Deep learning fundamentals: neural network architecture, layers, and training workflows
• Convolutional Neural Networks (CNNs) for image classification, using pre-trained models (ResNet, GoogLeNet, AlexNet)
• Sequence-to-sequence networks for time-series and text processing
• Transfer learning: adapting pre-trained models to custom datasets
• Designing deep networks: layer-by-layer construction with layerPlot and layerGraph
• Managing training: mini-batch size, learning rate schedules, and GPU acceleration

6.3 Python Integration and Hybrid AI Workflows

• Calling Python from MATLAB: importing Python classes, modules, and libraries
• Using Python deep learning frameworks (TensorFlow, PyTorch) within MATLAB workflows
• Using Python ML libraries (scikit-learn, pandas) for data preprocessing
• Facilitating two-way data exchange between MATLAB arrays and Python ndarrays
• Building hybrid AI pipelines that leverage MATLAB's engineering strengths and Python's AI ecosystem

Market-Aligned Competencies: Machine Learning in MATLAB, Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks, Convolutional Neural Networks (CNN), Transfer Learning, Time Series ML, Feature Engineering, Model Validation and Accuracy Assessment, Python-MATLAB Interoperability, Python Integration for AI/ML, TensorFlow and PyTorch in MATLAB, Predictive Analytics, Engineering AI Solutions, Hybrid Deep Learning Workflows, Pre-Trained Model Adaptation, Neural Network Architecture Design

Module 7: GPU Computing, Deployment, and Enterprise Integration

Covers high-performance computing with GPU acceleration, code generation for production deployment, App distribution, simulation-based design, and enterprise-grade deployment patterns essential for senior MATLAB engineers and team leads.

7.1 GPU-Accelerated and Parallel Computing

• Checking GPU availability and creating GPU arrays (gpuArray)
• Utilizing GPU-accelerated built-in functions: automatically accelerated math and deep learning
• Parallel Computing Toolbox: using parfor for loop parallelization
• Implementing SPMD (Single Program Multiple Data) and distributed arrays for HPC
• Cluster computing and using MATLAB Parallel Server for large-scale computing

7.2 Code Generation and Deployment

• MATLAB Coder: generating C/C++ code from MATLAB functions for embedded and production systems
• Generating MATLAB Coder reports: analyzing code generation, optimization opportunities, and compatibility checks
• MATLAB Compiler: packaging MATLAB applications as standalone executables and shared libraries
• Java and .NET interoperability for enterprise integration
• MATLAB Production Server: deploying MATLAB code as REST web services on enterprise infrastructure

7.3 MATLAB App Distribution and Sharing

• Publishing MATLAB Apps for internal organizational distribution
• Sharing MATLAB Online apps via MATLAB Drive
• Creating custom toolboxes with App Builder and App Designer

7.4 Simulink for Model-Based Design (MBD)

• Code generation from Simulink models (Simulink Coder / Embedded Coder)
• Hardware-in-the-loop (HIL) and model-in-the-loop (MIL) testing
• Using Simulink for automotive, aerospace, and robotics system simulation
• Stateflow: modeling state machines for control logic and event-driven systems

7.5 IoT and Embedded Systems

• li>Connecting MATLAB to physical hardware: supporting Arduino, Raspberry Pi, and BeagleBone packages
• Reading sensor data in real-time: temperature, accelerometer, gyroscope, ultrasonic, and IMU
• Generating C code for embedded ARM processors and deploying to microcontrollers

Market-Aligned Competencies: GPU-Accelerated Computing, Parallel Computing, High-Performance Computing (HPC), Cluster Computing, MATLAB Coder for C/C++ Code Generation, MATLAB Compiler, Standalone Application Deployment, MATLAB Production Server, REST API Service Deployment, Embedded Systems Development, Hardware-in-the-Loop (HIL) Testing, Model-Based Systems Engineering (MBSE), Stateflow Modeling, Simulink Code Generation, IoT Sensor Integration, Edge Computing, Real-Time Data Acquisition, Enterprise MATLAB Integration, Team and Organizational MATLAB Deployment, ARM Microcontroller Development

Module 8: Domain-Specific Applications and Capstone Project

Applies MATLAB across industry domains most relevant to job markets (engineering, finance, data science, and biomedical), culminating in a hands-on capstone that integrates every skill into a complete technical computing solution.

8.1 Domain-Specific MATLAB Applications

• Financial engineering with MATLAB: portfolio optimization, risk analysis, Monte Carlo simulation, and option pricing (Black-Scholes)
• Biomedical signal processing: ECG/EEG signal filtering, feature extraction, and visualization
• Engineering simulation: mechanical, electrical, and thermal system modeling
• Statistical analysis and hypothesis testing for research and quality assurance

8.2 Capstone Project: End-to-End MATLAB Solution

• Completing a real-world scenario: ingesting sensor or experimental data, cleaning and analyzing it, building a predictive model, and generating an interactive dashboard app
• Implementing a MATLAB class-based solution for the problem domain
• Creating a Simulink model of the system under study
• Applying deep learning for pattern recognition on the dataset
• Generating a comprehensive technical report from a Live Script
• Documenting the workflow and deploying the solution to a production-like environment

8.3 Professional MATLAB Development Practices

• Adhering to coding standards: MATLAB style guide (naming, formatting, commenting conventions)
• Building and documenting MATLAB toolboxes for team reuse
• Managing large MATLAB projects: folder organization, dependencies, and CI/CD

Market-Aligned Competencies: Capstone Solution Delivery, Financial Engineering and Quantitative Analysis, Biomedical Signal Processing, Portfolio Risk Analysis, Monte Carlo Simulation, Options Pricing, Statistical Hypothesis Testing, MATLAB Application Development, MATLAB Coding Standards, Technical Documentation and Reporting, Professional MATLAB Architecture, Engineering Simulation and Modeling, Computational Finance, Quality Assurance Analytics, MATLAB Tooling and Workflow Management, MATLAB Team Collaboration and Governance, Enterprise Data Analytics