Basic MATLAB Programming Training Course

Module 1: MATLAB Environment, Workflows, and Data Foundation

Build mastery of the MATLAB development ecosystem, covering both desktop and cloud workflows, 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

• Navigate the MATLAB desktop environment: Command Window, Editor, Workspace, Current Folder, and Command History
• Utilize MATLAB Online for cloud-based development, collaboration via MATLAB Drive, and cross-device accessibility
• Manage workspaces, search paths, and environment configurations
• Use shortcuts, profiles, and customize the development environment to boost engineering efficiency

1.2 Core Data Types and Mathematical Foundations

• Understand literals, variables, naming conventions, and assignment in MATLAB
• Create, index, and manipulate scalars, vectors, matrices, and multidimensional arrays
• Work with constants, operators, and built-in mathematical functions
• Distinguish between array operations (element-wise) and matrix operations (linear algebra)
• Apply logical indexing, relational operators, and logical arrays for advanced data filtering
• Organize complex data using cell arrays, structures (structs), and handle objects
• Utilize tables and timetables: MATLAB's modern paradigm for tabular, time-series, and experimental data

1.3 File I/O and Data Interoperability

• Import and export CSV, TXT, and delimited text files
• Perform read, write, and formatting operations on Excel spreadsheets
• Understand MAT native file formats (.mat) and workspace persistence
• Use the Import Wizard for automated data import generation
• Establish database connectivity to SQL Server, Oracle, PostgreSQL, and cloud databases
• Fetch web data, including 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

Enhance 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

• Determine when to use scripts versus functions and apply best practices for each
• Implement conditional logic using if/else, switch/case, and nested conditions
• Utilize loops (for, while) and apply loop optimization strategies, focusing on vectorization versus iteration
• Manage control flow within subfunctions and nested functions
• Employ error handling and debugging techniques such as try/catch, assert, dbstop, and the MATLAB Debugger

2.2 Function Programming and Code Organization

• li>Create functions, manage input/output arguments, and leverage varargin/varargout for flexibility
• Use anonymous functions and function handles to implement functional programming in MATLAB
• Work with subfunctions, local functions, and nested functions
• Organize files using packages and folder-level package management
• Understand the differences between pass-by-value and pass-by-reference (handle objects)

2.3 Object-Oriented Programming in MATLAB

• Define classes with properties, methods, and access levels (public/private/protected)
• Distinguish between handle classes (reference semantics) and value classes (value semantics)
• Manage object lifecycles using constructors and destructors
• Implement inheritance, method overriding, and abstract classes
• Utilize interface implementation and event handling within MATLAB classes
• Apply static methods, dynamic properties, and property validation

2.4 Profiling, Code Quality, and Testing

• Use the MATLAB profiler to identify bottlenecks and optimize compute-intensive code
• Analyze code coverage and utilize the MTest unit testing framework
• Integrate version control systems like Git and SVN into the MATLAB Editor workflow
• Understand Continuous Integration (CI/CD) concepts using Jenkins and the MATLAB CI Pipeline
• Address static code analysis warnings and adhere 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

Cover 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

• Create 2D plots: line plots, scatter plots, bar charts, pie charts, area plots, and error bars
• Manage multi-axis plotting using hold, subplot, tiledlayout, and axes positioning
• Visualize 3D data using surf, mesh, contour, slice, and volume visualization tools
• Customize plots with titles, labels, legends, annotations, line styles, markers, and colors
• Utilize colormaps, colorbars, and ensure perceptually accurate plots
• Export high-resolution figures for publications in formats such as PNG, PDF, SVG, and EMF

3.2 Interactive Visualization and Dashboards

• Customize figures with UI controls including sliders, buttons, dropdowns, and callbacks
• Use MATLAB App Designer to build interactive desktop applications with drag-and-drop UI components
• Enable plot interactions such as zoom, pan, brushing, and selection callbacks
• Deploy MATLAB visualizations as online interactive dashboards as web apps

3.3 Live Scripts and Automated Reporting

• Use MATLAB Live Script (.mlx) to create executable notebooks that combine code, plots, and formatted text
• Support Markdown and LaTeX in Live Scripts for mathematical equations
• Create custom Live Script sections, manage input parameters, and establish sharing workflows
• Automate 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

Provide comprehensive coverage of linear algebra as the mathematical core of MATLAB, linear programming optimization, and symbolic computation for analytical solutions. These skills are essential for engineering, operations research, and scientific modeling applications.

4.1 Linear Algebra and Matrix Operations

• Construct matrices using eye, zeros, ones, rand, randn, diag, and other special matrix generators
• Perform matrix decomposition: LU, QR, Cholesky, SVD, and eigenvalue analysis
• Apply special functions such as det, trace, rank, norm, condition number, and pseudo-inverse
• Solve linear systems using left division (\), mldivide, and least squares solutions
• Analyze eigenvalues and eigenvectors, and apply matrix functions like expm, logm, and sqrtm
• Perform sparse matrix operations for memory-efficient computing

4.2 Optimization Fundamentals

• Use linprog for constrained linear programming optimization
• Apply nonlinear optimization tools: fmincon, fminsearch, and fzero
• Perform curve fitting and parameter estimation using fit, polyfit, and lsqcurvefit
• Gain an introduction to the Optimization Toolbox workflow

4.3 Symbolic Mathematics

• Create symbolic variables and manipulate symbolic expressions
• Perform analytical differentiation and integration using dsolve and int
• Use variable-precision arithmetic (vpa) for high-precision computation
• Compute Laplace and Fourier transforms in symbolic mode
• Solve 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

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

5.1 Signal Processing Fundamentals

• Understand sampling theory: sampling rate, aliasing, and the Nyquist criterion
• Generate fundamental signals: sine, cosine, square, sawtooth, and chirp signals
• li>Fundamental signal generation: sine, cosine, square, sawtooth, and chirp signals
• Conduct frequency domain analysis using FFT, spectrograms, and magnitude/phase plots
• Design filters: lowpass, highpass, bandpass, bandstop FIR and IIR filters
• Analyze spectra, power spectral density, and apply filtering techniques
• Denoise, smooth, and detect envelopes in signals

5.2 Image and Video Processing

• Create, read, write, and display images using the MATLAB Image Processing Toolbox
• Enhance images through contrast adjustment, histogram equalization, and filtering
• Segment images using thresholding, edge detection, and watershed algorithms
• Perform geometric transformations and image registration
• Execute morphological operations: dilation, erosion, opening, and closing
• Detect features such as corners (Harris), blobs, and template matches

5.3 Introduction to Simulink and System Modeling

• Navigate the Simulink environment: create models, use the blocks library, and route signals
• Build block diagrams with sources, sinks, continuous/discrete blocks, and integrators
• Set simulation parameters: solver selection, step size, and simulation duration
• Create reusable components using subsystems, masks, and library blocks
• Analyze models using scopes, diagnostic messages, and the Model Explorer
• Introduce Simulink for control systems, including plant modeling and controller simulation

5.4 Control Systems and Dynamical Systems

• Define transfer functions and block diagrams within the Control System Toolbox
• Analyze step, impulse, frequency (Bode), and root locus responses
• Design and tune PID controllers
• Represent and analyze systems using state-space models

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

Explore 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 one of the most in-demand technical skill sets in engineering today.

6.1 Classical Machine Learning with MATLAB

• Implement classification algorithms: KNN, Naive Bayes, SVM, decision trees, and ensemble methods
• Apply regression algorithms: linear regression, polynomial regression, and regularized regression
• Perform unsupervised learning: clustering (k-means, hierarchical), PCA, and dimensionality reduction
• Validate models using cross-validation, confusion matrices, ROC curves, and accuracy metrics
• Manage feature selection, data preprocessing, and train/validation/test splitting

6.2 Deep Learning in MATLAB

• Understand deep learning fundamentals: neural network architecture, layers, and training workflows
• Build Convolutional Neural Networks (CNNs) for image classification using pre-trained models (ResNet, GoogLeNet, AlexNet)
• Utilize sequence-to-sequence networks for time-series and text processing
• Apply transfer learning to adapt pre-trained models to custom datasets
• Design deep networks layer-by-layer using layerPlot and layerGraph
• Manage training processes including mini-batch size, learning rate schedules, and GPU acceleration

6.3 Python Integration and Hybrid AI Workflows

• Call Python from MATLAB by importing Python classes, modules, and libraries
• Use Python deep learning frameworks (TensorFlow, PyTorch) within MATLAB workflows
• Leverage Python ML libraries (scikit-learn, pandas) for data preprocessing
• Exchange data two-way between MATLAB arrays and Python ndarrays
• Build hybrid AI pipelines that combine MATLAB's engineering strengths with 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

Cover 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

• Check GPU availability and create GPU arrays using gpuArray
• Utilize GPU-accelerated built-in functions for automatically accelerated math and deep learning
• Parallelize loops using parfor in the Parallel Computing Toolbox
• Apply SPMD (Single Program Multiple Data) and distributed arrays for High-Performance Computing (HPC)
• Implement cluster computing and use MATLAB Parallel Server for large-scale computing

7.2 Code Generation and Deployment

• Use MATLAB Coder to generate C/C++ code from MATLAB functions for embedded and production systems
• Analyze code generation, optimization opportunities, and compatibility checks via MATLAB Coder reports
• Package MATLAB applications as standalone executables and shared libraries using MATLAB Compiler
• Integrate with Java and .NET for enterprise systems
• Deploy MATLAB code as REST web services on enterprise infrastructure using MATLAB Production Server

7.3 MATLAB App Distribution and Sharing

• Publish MATLAB Apps for internal organizational distribution
• Share MATLAB Online apps via MATLAB Drive
• Create custom toolboxes using App Builder and App Designer

7.4 Simulink for Model-Based Design (MBD)

• Generate code from Simulink models using Simulink Coder / Embedded Coder
• Perform Hardware-in-the-loop (HIL) and Model-in-the-loop (MIL) testing
• Apply Simulink for automotive, aerospace, and robotics system simulation
• Use Stateflow for state machine modeling in control logic and event-driven systems

7.5 IoT and Embedded Systems

• li>Connect MATLAB to physical hardware using support packages for Arduino, Raspberry Pi, and BeagleBone
• Read sensor data in real-time, including temperature, accelerometer, gyroscope, ultrasonic, and IMU inputs
• Generate C code for embedded ARM processors and deploy 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

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

8.1 Domain-Specific MATLAB Applications

• Perform financial engineering with MATLAB: portfolio optimization, risk analysis, Monte Carlo simulation, and option pricing (Black-Scholes)
• Process biomedical signals: filter ECG/EEG signals, extract features, and visualize data
• Conduct engineering simulations for mechanical, electrical, and thermal systems
• Perform statistical analysis and hypothesis testing for research and quality assurance

8.2 Capstone Project: End-to-End MATLAB Solution

• Address a 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

• Adhere to coding standards including the MATLAB style guide (naming, formatting, commenting conventions)
• Build and document MATLAB toolboxes for team reuse
• Manage large MATLAB projects through folder organization, dependency management, 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