MATLAB Course

Lesson 1 • Vectors and Matrices Basics

Teaches creation, indexing, and basic manipulation of vectors and matrices. Provides the core data structure used throughout MATLAB programming.

Lesson 2 • Scripts and the MATLAB Path

Explains how to write, save, and execute script files and manage the search path. Enables organised, reproducible workflows from the start.

Lesson 3 • Variables, Data Types, and Operators

Introduces numeric, character, logical, and complex data types alongside arithmetic and relational operators. Forms the data-handling foundation for every later topic.

Lesson 4 • Navigating the MATLAB Interface

Covers the Command Window, Editor, Workspace, and Current Folder panels. Establishes the working environment needed for all subsequent exercises.

Lesson 1 • Sparse Matrices and Performance

Teaches sparse matrix storage, creation, and efficient operations for large datasets. Prepares learners to handle memory-intensive linear algebra problems.

Lesson 2 • Eigenvalues and Decompositions

Introduces eigenvalue analysis, LU, QR, and singular value decompositions. Provides tools for dimensionality reduction and stability analysis.

Lesson 3 • Advanced Matrix Manipulation

Covers reshaping, concatenation, transposition, and logical indexing of matrices. Extends basic matrix skills into flexible data-restructuring techniques.

Lesson 4 • Matrix Arithmetic and Element-Wise Ops

Distinguishes matrix multiplication from element-wise operations and covers division and power variants. Prevents common errors in numerical computations.

Lesson 1 • Error Handling and Debugging

Introduces try-catch blocks, error messages, and the MATLAB debugger. Builds habits for writing robust, fault-tolerant programs.

Lesson 2 • Loop Structures

Teaches for and while loops, including break and continue control. Provides iteration tools essential for numerical algorithms and data processing.

Lesson 3 • Code Style and Best Practices

Establishes naming conventions, modular design, and code readability standards. Ensures maintainable code suitable for collaborative and professional environments.

Lesson 4 • Conditional Statements

Covers if-elseif-else and switch-case constructs for decision-making. Enables programs to respond dynamically to varying input conditions.

Lesson 1 • Variable Scope and Workspaces

Explains local, global, and persistent variables and their scope rules. Prevents subtle bugs caused by unintended variable sharing.

Lesson 2 • Anonymous and Nested Functions

Introduces anonymous function handles and nested function definitions. Enables compact, flexible function design for callbacks and numerical solvers.

Lesson 3 • Writing and Calling Functions

Covers function syntax, input/output arguments, and calling conventions. Establishes the building block of modular MATLAB programming.

Lesson 4 • Function Documentation and Testing

Covers H1 lines, help text blocks, and basic unit testing with assert. Ensures functions are self-documenting and verifiably correct.

Lesson 1 • Reading and Writing Text Files

Covers importdata, readmatrix, writematrix, and low-level file I/O for text formats. Connects raw data files to the MATLAB workspace.

Lesson 2 • Spreadsheet and Binary Data

Teaches readtable, writetable, and MAT-file operations for Excel and binary formats. Enables seamless data exchange with common office and scientific tools.

Lesson 3 • Structures and Cell Arrays

Covers struct and cell array creation, access, and manipulation for heterogeneous data. Extends data organisation beyond uniform numeric arrays.

Lesson 4 • Database and Web Data Access

Introduces MATLAB's database connectivity and web API calls using webread. Enables integration of live external data sources into workflows.

Lesson 1 • Specialised and Interactive Plots

Introduces heatmaps, geographic plots, and interactive data cursors. Expands the visualisation toolkit for domain-specific analytical needs.

Lesson 2 • 3D Visualisation Techniques

Covers surf(), mesh(), contour(), and 3D scatter plots for spatial data. Extends visualisation capability to three-dimensional engineering datasets.

Lesson 3 • 2D Plotting Fundamentals

Covers plot(), scatter(), bar(), and histogram() for standard 2D charts. Establishes core visualisation skills applied throughout the course.

Lesson 4 • Figure Customisation and Annotation

Teaches axes properties, legends, colormaps, and text annotations. Produces clear, professional figures suitable for reports and publications.

Lesson 1 • Interpolation and Curve Fitting

Covers interp1, interp2, and polyfit for data interpolation and regression. Provides tools for estimating values between measured data points.

Lesson 2 • Fourier Analysis and Signal Processing

Covers FFT, power spectral density, and basic filtering operations. Introduces frequency-domain analysis for signal and data processing tasks.

Lesson 3 • Numerical Integration and Differentiation

Teaches integral(), trapz(), and gradient() for computing areas and derivatives. Connects calculus concepts to practical numerical computation.

Lesson 4 • Root Finding and Optimisation

Applies fzero, fsolve, fminbnd, and fminunc to locate roots and optima. Enables automated solution of nonlinear equations and objective minimisation.

Lesson 1 • Introduction to MATLAB Classes

Explains the classdef syntax, value vs. handle classes, and basic class structure. Establishes the conceptual foundation for object-oriented design in MATLAB.

Lesson 2 • Methods and Operator Overloading

Covers ordinary methods, static methods, and overloading arithmetic operators. Enables intuitive, domain-specific behaviour for custom class objects.

Lesson 3 • Inheritance and Polymorphism

Teaches single and multiple inheritance, method overriding, and abstract classes. Supports hierarchical design patterns for extensible software.

Lesson 4 • Designing a Complete OOP Application

Integrates classes, inheritance, and events into a cohesive mini-application. Consolidates all OOP concepts through a realistic end-to-end design exercise.