Basic MATLAB Programming Training Course

Module 1: MATLAB Environment, Workflows, and Data Foundation

Establishes mastery of the MATLAB development ecosystem, encompassing both desktop and cloud-based workflows. Topics include core data types, file input/output (I/O), and data management strategies that serve as the foundation for all 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, MATLAB Drive collaboration, and cross-device accessibility
• Managing the workspace, search paths, and environment configuration
• Utilizing shortcuts, profiles, and customizing the development environment for engineering efficiency

1.2 Core Data Types and Mathematical Foundations

• Literals, variables, naming conventions, and assignment in MATLAB
• Scalars, vectors, matrices, and multidimensional arrays: creation, indexing, and manipulation
• Constants, operators, and built-in mathematical functions
• Distinguishing array versus matrix operations: element-wise versus linear algebra
• Logical indexing, relational operators, and logical arrays for advanced filtering
• Using cell arrays, structures, and handle objects for complex data organization
• Tables and timetables: MATLAB's modern approach to tabular data for time-series and experimental data

1.3 File I/O and Data Interoperability

• Importing and exporting CSV, TXT, and delimited text files
• Working with Excel spreadsheets: performing read, write, and formatting operations
• Understanding MAT native file formats (.mat) and workspace persistence
• Utilizing the Import wizard and generating automated data import scripts
• Database connectivity: connecting to SQL Server, Oracle, PostgreSQL, and cloud databases
• Web data integration: fetching JSON, XML, and REST API responses in 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

Deepens 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 vs. functions: guidelines for usage and best practices
• Conditional logic: implementing if/else, switch/case, and nested conditions
• Loops: using for and while loops, along with optimization strategies (vectorization versus iteration)
• Managing control flow in subfunctions and nested functions
• Error handling and debugging techniques: using try/catch, assert, dbstop, and the MATLAB Debugger

2.2 Function Programming and Code Organization

• Creating functions, managing input/output arguments, and utilizing varargin/varargout for flexibility
• Anonymous functions and function handles: introducing functional programming concepts in MATLAB
• Utilizing subfunctions, local functions, and nested functions
• Organizing files, packages, and folder-level package management
• Understanding pass-by-value versus pass-by-reference (using handle objects)

2.3 Object-Oriented Programming in MATLAB

• Defining classes with properties, methods, and access levels (public/private/protected)
• Distinguishing between handle classes and value classes: value semantics versus reference semantics
• Managing constructors, destructors, and object lifecycles
• Implementing inheritance, method overriding, and abstract classes
• Implementing 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 utilizing the MTest unit testing framework
• Integrating version control: implementing Git and SVN workflows in the MATLAB Editor
• Understanding Continuous Integration (CI/CD) concepts using Jenkins and MATLAB CI Pipeline
• 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 techniques, 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 techniques: 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: utilizing surf, mesh, contour, slice, and volume visualization
• Customizing plots: adding titles, labels, legends, annotations, line styles, markers, and colors
• Using colormaps, colorbars, and creating 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
• Enabling 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
• Incorporating Markdown and LaTeX support in Live Scripts for mathematical equations
• Creating custom Live Script sections, defining input parameters, and establishing sharing workflows
• Automated 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 module 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 techniques: LU, QR, Cholesky, SVD, and eigenvalue analysis
• Special functions: calculating det, trace, rank, norm, condition number, and pseudo-inverse
• Solving linear systems: using left division (\), mldivide, and least squares solutions
• Understanding eigenvalues, 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 with dsolve and int
• Using variable-precision arithmetic (vpa) for high-precision computation
• Calculating 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 demanded in telecommunications, audio processing, biomedical engineering, and industrial inspection sectors.

5.1 Signal Processing Fundamentals

• Sampling theory: understanding sampling rate, aliasing, and the Nyquist criterion
• Fundamental signal generation: creating sine, cosine, square, sawtooth, and chirp signals
• Fundamental signal generation: creating sine, cosine, square, sawtooth, and chirp signals
• Frequency domain analysis: using FFT, spectrogram, and magnitude/phase plots
• Filter design: designing lowpass, highpass, bandpass, bandstop FIR and IIR filters
• Performing spectral analysis, calculating power spectral density, and applying filtering techniques
• 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: adjusting contrast, performing histogram equalization, and filtering
• Image segmentation: applying thresholding, edge detection, and watershed algorithms
• Performing 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: utilizing sources, sinks, continuous/discrete blocks, and integrators
• Setting simulation parameters: solver selection, step size, and simulation duration
• Creating subsystems, masks, and library blocks for reusable components
• Analyzing models 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

• Working with transfer functions and block diagrams in the Control System Toolbox
• Analyzing step, impulse, frequency (Bode), and root locus responses
• Fundamentals of PID controller design and tuning
• Understanding 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, ranging 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 set 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 splitting data into train/validation/test sets

6.2 Deep Learning in MATLAB

• Deep learning fundamentals: neural network architecture, layers, and training workflows
• Convolutional Neural Networks (CNNs) for image classification, utilizing pre-trained models (ResNet, GoogLeNet, AlexNet)
• Sequence-to-sequence networks for processing time-series and text data
• Transfer learning: adapting pre-trained models to custom datasets
• Deep network design: layer-by-layer construction using layerPlot and layerGraph
• Training management: configuring 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
• Utilizing Python ML libraries (scikit-learn, pandas) for data preprocessing
• Performing two-way data exchange between MATLAB arrays and Python ndarrays
• Building hybrid AI pipelines that leverage MATLAB's engineering strengths alongside 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 operations
• Parallel Computing Toolbox: implementing parfor for loop parallelization
• Using SPMD (Single Program Multiple Data) and distributed arrays for HPC
• Implementing cluster computing and 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
• Reviewing MATLAB Coder reports to analyze code generation, optimization opportunities, and compatibility
• MATLAB Compiler: packaging MATLAB applications as standalone executables and shared libraries
• Enabling 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 using App Builder and App Designer

7.4 Simulink for Model-Based Design (MBD)

• Generating code from Simulink models (Simulink Coder / Embedded Coder)
• Performing hardware-in-the-loop (HIL) and model-in-the-loop (MIL) testing
• Utilizing Simulink for automotive, aerospace, and robotics system simulation
• Using Stateflow for state machine modeling in control logic and event-driven systems

7.5 IoT and Embedded Systems

• Connecting MATLAB to physical hardware: utilizing Arduino, Raspberry Pi, and BeagleBone support 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: filtering, feature extraction, and visualizing ECG/EEG signals
• Engineering simulation: modeling mechanical, electrical, and thermal systems
• Conducting statistical analysis and hypothesis testing for research and quality assurance

8.2 Capstone Project: End-to-End MATLAB Solution

• Addressing a complete 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 specific 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: following the MATLAB style guide (naming, formatting, commenting conventions)
• Building and documenting MATLAB toolboxes for team reuse
• Managing large MATLAB projects: organizing folders, handling dependencies, and implementing 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