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 workflows, core data types, file I/O, and data management strategies that form the foundation for all advanced technical computing tasks.

1.1 The MATLAB Ecosystem: Desktop, Online, and Drive

• Working with 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
• Workspace management, search paths, and environment configuration
• 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
• Array vs. matrix operations: element-wise vs. linear algebra
• Logical indexing, relational operators, and logical arrays for advanced filtering
• Cell arrays, structures, structs, and handle objects for complex data organization
• Tables and timetables: MATLAB's modern tabular data paradigm 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: read, write, and format operations
• MAT native file formats (.mat) and workspace persistence
• Import wizard and automated data import generation
• Database connectivity: connecting to SQL Server, Oracle, PostgreSQL, and cloud databases
• Web data: 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 maintainable technical codebases.

2.1 Structured Programming and Control Flow

• Scripts vs. functions: when to use each and best practices
• Conditional logic: if/else, switch/case, and nested conditions
• Loops: for, while, and loop optimization strategies (vectorization vs. iteration)
• Control flow in subfunctions and nested functions
• Error handling and debugging techniques: try/catch, assert, dbstop, and MATLAB Debugger

2.2 Function Programming and Code Organization

• Function creation, input/output arguments, and varargin/varargout flexibility
• Anonymous functions and function handles: functional programming in MATLAB
• Subfunctions, local functions, and nested functions
• File-based organization, packages, and folder-level package management
• Pass-by-value vs. pass-by-reference (handle objects)

2.3 Object-Oriented Programming in MATLAB

• Classes: defining properties, methods, and access levels (public/private/protected)
• Handle classes vs. value classes: value semantics vs. reference semantics
• Constructors, destructors, and object lifecycle management
• Inheritance, method overriding, and abstract classes
• Interface implementation and event handling in MATLAB classes
• Static methods, dynamic properties, and properties validation

2.4 Profiling, Code Quality, and Testing

• MATLAB profiler: identifying bottlenecks and optimizing compute-intensive code
• Code coverage analysis and MTest unit testing framework
• Version control integration: Git and SVN workflow in MATLAB Editor
• Continuous Integration (CI/CD) concepts with Jenkins and MATLAB CI Pipeline
• Static code analysis warnings and 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: 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
• Colormaps, colorbars, and perceptually accurate plots
• Exporting high-resolution figures for publications: formats (PNG, PDF, SVG, EMF)

3.2 Interactive Visualization and Dashboards

• Figure customization with UI controls: sliders, buttons, dropdowns, and callbacks
• MATLAB App Designer: building interactive desktop applications with drag-and-drop UI components
• Plot interactions: zoom, pan, brushing, and selection callbacks
• 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
• Markdown and LaTeX support in Live Scripts for mathematical equations
• Custom Live Script sections, input parameters, and 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

Comprehensive coverage of linear algebra as the mathematical core of MATLAB, linear programming optimization, and symbolic computation for analytical solutions. Essential for engineering, operations research, and scientific modeling applications.

4.1 Linear Algebra and Matrix Operations

• Matrix construction: 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: left division (\), mldivide and least squares solutions
• Eigenvalues, eigenvectors, and matrix function applications (expm, logm, sqrtm)
• Sparse matrix operations and memory-efficient computing

4.2 Optimization Fundamentals

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

4.3 Symbolic Mathematics

• Symbolic variable creation and symbolic expression manipulation
• Analytical differentiation and integration with dsolve and int
• Variable-precision arithmetic (vpa) for high-precision computation
• Laplace and Fourier transforms in symbolic mode
• Solving equations analytically: 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: sampling rate, aliasing, and Nyquist criterion
• Fundamental signal generation: sine, cosine, square, sawtooth, and chirp signals
• 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

• Image creation, reading, writing, and display with MATLAB Image Processing Toolbox
• Image enhancement: contrast adjustment, histogram equalization, and filtering
• Image segmentation: thresholding, edge detection, and watershed
• 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

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

5.4 Control Systems and Dynamical Systems

• Transfer functions and block diagrams in the Control System Toolbox
• Step, impulse, frequency (Bode), and root locus analysis
• PID controller design and tuning fundamentals
• 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 capability within MATLAB, from classical supervised/unsupervised learning to deep neural networks, pre-trained models, and integration with Python for hybrid AI workflows. 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 train/validation/test splitting

6.2 Deep Learning in MATLAB

• Deep learning fundamentals: neural network architecture, layers, and training workflow
• 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
• Deep network design: layer-by-layer construction with layerPlot and layerGraph
• Training management: 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
• Two-way data exchange between MATLAB arrays and Python ndarrays
• Building hybrid AI pipelines leveraging 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 that are essential for senior MATLAB engineers and team leads.

7.1 GPU-Accelerated and Parallel Computing

• Checking GPU availability and GPU array creation (gpuArray)
• GPU-accelerated built-in functions: automatically accelerated math and deep learning
• Parallel Computing Toolbox: parfor for loop parallelization
• SPMD (Single Program Multiple Data) and distributed arrays for HPC
• 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
• 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
• Simulink for automotive, aerospace, and robotics system simulation
• Stateflow: state machine modeling for control logic and event-driven systems

7.5 IoT and Embedded Systems

• Connecting MATLAB to physical hardware: Arduino, Raspberry Pi, and BeagleBone support packages
• Reading sensor data in real-time: temperature, accelerometer, gyroscope, ultrasonic, and IMU
• Generating C 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

• Complete real-world scenario: ingest sensor or experimental data, clean and analyze it, build a predictive model, and generate an interactive dashboard app
• Implement a MATLAB class-based solution for the problem domain
• Create a Simulink model of the system under study
• Apply deep learning for pattern recognition on the dataset
• Generate a comprehensive technical report from a Live Script
• Document the workflow and deploy the solution to a production-like environment

8.3 Professional MATLAB Development Practices

• 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