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

• 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 accessibility
• Managing workspaces, search paths, and environment configuration
• Utilizing shortcuts, profiles, and customizing the development environment for enhanced 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 vs. matrix operations: element-wise vs. linear algebra
• Logical indexing, relational operators, and logical arrays for advanced filtering
• Using 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
• Using MAT native file formats (.mat) and workspace persistence
• Leveraging the Import wizard and automated data import generation
• Database connectivity: connecting to SQL Server, Oracle, PostgreSQL, and cloud databases
• Fetching web data: retrieving 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

• Distinguishing scripts vs. functions: usage scenarios and best practices
• Conditional logic: if/else, switch/case, and nested conditions
• Loops: for, while, and loop optimization strategies (vectorization vs. iteration)
• Managing control flow in subfunctions and nested functions
• Error handling and debugging techniques: 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 flexibility
• Implementing anonymous functions and function handles: functional programming in MATLAB
• Utilizing subfunctions, local functions, and nested functions
• File-based organization, packages, and folder-level package management
• Distinguishing pass-by-value vs. pass-by-reference (handle objects)

2.3 Object-Oriented Programming in MATLAB

• Defining classes: properties, methods, and access levels (public/private/protected)
• Differentiating handle classes vs. value classes: value semantics vs. reference semantics
• Managing constructors, destructors, and object lifecycle
• Implementing inheritance, method overriding, and abstract classes
• Implementing interfaces and event handling in MATLAB classes
• Utilizing static methods, dynamic properties, and properties 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: Git and SVN workflows within the MATLAB Editor
• Understanding Continuous Integration (CI/CD) concepts with Jenkins and the 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, 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: surf, mesh, contour, slice, and volume visualization
• Customizing plots: titles, labels, legends, annotations, line styles, markers, and colors
• Utilizing colormaps, colorbars, and creating perceptually accurate plots
• Exporting high-resolution figures for publications in formats like 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
• Deploying web apps: publishing MATLAB visualizations as online interactive dashboards

3.3 Live Scripts and Automated Reporting

• Using MATLAB Live Scripts (.mlx): executable notebooks combining code, plots, and formatted text
• Incorporating Markdown and LaTeX support in Live Scripts for mathematical equations
• Customizing Live Script sections, input parameters, and sharing workflows
• Automating report generation by 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. 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: left division (\), mldivide, and least squares solutions
• Working with eigenvalues, eigenvectors, and matrix function applications (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
• Computing 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

• Understanding sampling theory: sampling rate, aliasing, and Nyquist criterion
• Generating fundamental signals: sine, cosine, square, sawtooth, and chirp signals
• Fundamental signal generation: sine, cosine, square, sawtooth, and chirp signals
• Analyzing frequency domains: FFT, spectrogram, and magnitude/phase plots
• Designing filters: lowpass, highpass, bandpass, bandstop FIR and IIR filters
• Conducting spectral analysis, power spectral density, and filtering applications
• Performing signal denoising, smoothing, and envelope detection

5.2 Image and Video Processing

• Creating, reading, writing, and displaying images with the MATLAB Image Processing Toolbox
• Enhancing images: contrast adjustment, histogram equalization, and filtering
• Segmenting images: thresholding, edge detection, and watershed techniques
• Performing geometric transformations and image registration
• Applying morphological operations: dilation, erosion, opening, and closing
• Detecting features: 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
• Configuring simulation parameters: solver selection, step size, and simulation duration
• Creating reusable components using subsystems, masks, and library blocks
• 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
• Representing and analyzing systems using state-space methods

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/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

• Implementing classification algorithms: KNN, Naive Bayes, SVM, decision trees, and ensemble methods
• Applying regression algorithms: linear regression, polynomial regression, and regularized regression
• Performing unsupervised learning: clustering (k-means, hierarchical), PCA, and dimensionality reduction
• Validating models using cross-validation, confusion matrices, ROC curves, and accuracy metrics
• Selecting features, preprocessing data, and splitting into train/validation/test sets

6.2 Deep Learning in MATLAB

• Understanding deep learning fundamentals: neural network architecture, layers, and training workflows
• Building Convolutional Neural Networks (CNNs) for image classification using pre-trained models (ResNet, GoogLeNet, AlexNet)
• Implementing sequence-to-sequence networks for time-series and text processing
• Utilizing transfer learning to adapt pre-trained models to custom datasets
• Designing deep networks layer-by-layer 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
• Leveraging 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
• Using the Parallel Computing Toolbox: parfor for loop parallelization
• Implementing SPMD (Single Program Multiple Data) and distributed arrays for HPC
• Engaging in cluster computing and using MATLAB Parallel Server for large-scale computing

7.2 Code Generation and Deployment

• Using MATLAB Coder to generate C/C++ code from MATLAB functions for embedded and production systems
• Analyzing MATLAB Coder reports: reviewing code generation, optimization opportunities, and compatibility checks
• Using MATLAB Compiler to package MATLAB applications as standalone executables and shared libraries
• Enabling Java and .NET interoperability for enterprise integration
• Deploying MATLAB code as REST web services on enterprise infrastructure using MATLAB Production Server

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)

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

7.5 IoT and Embedded Systems

• 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: filtering, feature extraction, and visualization of 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 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