Get started with MATLAB and ChatGPT for scientific coding

Access ChatGPT through OpenAI's site by signing up or logging in, completing phone verification, selecting a model like GPT-4, and using the browser chat interface for organized conversations.

Learn how to use ChatGPT as an integrated virtual tutor within MATLAB for scientific coding, with guidance to verify outputs and adapt to MATLAB 2019 and newer versions.

Learn how to use ChatGPT to explain MATLAB concepts, focusing on the colon operator to create vectors from start, step, and end. See prompts, explanations, and examples like counting numbers.

Learn how variables store numbers, strings, and arrays in Matlab, create and overwrite them, view them with the who command, and manage the Matlab buffer and workspace, including clearing variables.

Create numeric variables as scalars, vectors, and matrices using square brackets and semicolons for horizontal and vertical concatenation. Use transpose to make column vectors and colon to generate sequences.

Explore how Matlab stores strings and characters, learn about cell arrays, and master the distinction between square brackets and curly brackets for concatenation and indexing.

Use natural language to ask Matlab with ChatGPT to write starter code for a movie ratings structure, illustrating fields like rater name, movie name, and rating.

Understand how MATLAB functions work with inputs and outputs, from single to multiple inputs and outputs, how to call them, capture results, use help and square brackets for multiple outputs.

explore how the matlab path determines where matlab searches for functions and files, and learn practical methods to view and modify the path with set path, add path, and genpath.

Apply soft coding by placing all parameters at the top of the script so changes propagate across the codebase. Avoid hard coding to reduce errors and missed updates.

Master practical data import in MATLAB for scientific coding, from copying data from Excel into the workspace to using load for text files, uigetfile, .mat imports, and image loading.

Explore the architecture of Matlab control statements, focusing on if and switch statements, how booleans drive decisions with else and end, and case sensitivity.

Explore while loops in matlab, using a toggle and boolean condition to control iterations, compare them with for loops, and learn when each is preferred for known versus unknown counts.

Learn how try-catch statements in MATLAB manage errors, inspect the MATLAB exception structure, and decide when to allow errors to surface or continue across multiple data files.

Learn to export mixed text and numeric MATLAB data into a tab-delimited text file for spreadsheets, using headers, a name column, and looped fprint formatting.

Learn how to use ChatGPT to translate Python code into MATLAB, with step-by-step explanations and mapping of Python concepts to MATLAB equivalents like rand and exp for scientific coding.

Learn to create bar plots in Matlab with and without error bars, using vectors and matrices, and understand grouping, transposing, and adding error bars with the errorbar function.

Visualize matrices with imagesc, contourf, and surf, customize color maps and color limits, export figures in common formats like png or vector formats, and explore two dimensional gaussian and convolution.

Explore how MATLAB isolates variables in separate workspaces (stacks) for each function, transfer data via inputs and outputs, and debug with breakpoints, including conditional breakpoints.

Learn to implement input checks in basicstats.m by validating numeric inputs, requiring at least three numbers, and enforcing vector, not matrix, inputs, with helpful error messages and a help file.