ING3513 - Introduction to artificial intelligence and machine learning

Lab material for ING3513.

Setup Instructions

Prerequisites

Before starting, ensure you have:

• A GitHub account
• Access to the ing3513/course-materials repository

Installation Steps

1. Install WSL (Windows Subsystem for Linux)

   • Open PowerShell as Administrator
   • Run: wsl --install
   • Restart your computer when prompted

2. Install Visual Studio Code

   • Download from https://code.visualstudio.com/
   • Run the installer and follow the setup wizard

3. Install WSL Extension

   • Download from https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-wsl
   • Or in VS Code: Go to Extensions (Ctrl+Shift+X), search for "WSL" and install the official Microsoft WSL extension

4. Connect to WSL

   • Press F1 to open the Command Palette
   • Type and select: WSL: Connect to WSL
   • VS Code will reopen in your Linux home directory (~ or /home/)

5. Login with GitHub

   • Open the Accounts menu in VS Code (bottom left corner)
   • Select "Sign in with GitHub"
   • Follow the authentication process

6. Clone Repository

   • Open the Source Control view (Ctrl+Shift+G)
   • Click "Clone Repository" or press F1 and type Git: Clone
   • Enter the repository URL: https://github.com/ing3513/course-materials.git
   • Select a folder location and open the cloned repository

7. Install Recommended Extensions

   • When you open the repository, VS Code will prompt you to install recommended extensions
   • Click "Install" to automatically install Python, Jupyter, and WSL extensions
   • Alternatively, you can manually install them from the Extensions view (Ctrl+Shift+X)

8. Setup Python Environment

   • Open a terminal in VS Code (Ctrl+`)
   • Run the setup script: ./scripts/setupPythonEnvironment.sh
   • This will install uv, Python 3.13, and all project dependencies
   • If this is your first run, restart your terminal or run: source ~/.bashrc

9. Verify Installation

   • Open labs/lab00/lab00.ipynb in VS Code
   • Click "Select Kernel" in the top right corner of the notebook
   • Choose "Python Environments..." and select the ing3513 environment
   • Run the verification cell (click the play button or press Shift+Enter)
   • You should see pandas version information and a success message

Congratulations! You are now set up and ready to work with Jupyter notebooks in this course.

About the Python Environment

This course uses uv, a modern Python package and project manager. uv is significantly faster than pip and provides better dependency resolution.

Key Features

• Automatic Python version management
• Fast dependency resolution and installation
• Reproducible environments via lockfiles
• All-in-one tool replacing pip, venv, and virtualenv

Common Commands

• uv run <command> - Run a command in the project's virtual environment
• uv add <package> - Add a new dependency
• uv sync --locked - Sync dependencies from lockfile (use --locked to ensure exact versions match)
• uv python install - Install Python versions

Code Formatting and Linting

This project uses Ruff for code formatting and linting. Ruff is an extremely fast Python linter and formatter.

Automatic Formatting

VS Code is configured to automatically format your code on save, both in Python files and Jupyter notebooks. This includes:

• Code formatting (consistent style)
• Import sorting
• Auto-fixing common issues

Manual Commands

If you need to run formatting or linting manually:

• uv run ruff format . - Format all Python files
• uv run ruff check . - Check for linting issues
• uv run ruff check --fix . - Auto-fix linting issues where possible

Exporting Notebooks to HTML and PDF

Using VS Code (GUI)

1. Open the notebook in VS Code
2. Click the ... (More Actions) button in the notebook toolbar
3. Select Export from the dropdown menu
4. Choose HTML as the export format
5. Select a location to save the file

Using the Command Line

You can also export notebooks programmatically using nbconvert:

# Export a single notebook to HTML
uv run jupyter nbconvert --to html path/to/notebook.ipynb

# Export to a specific output directory
uv run jupyter nbconvert --to html --output-dir=./output path/to/notebook.ipynb

The exported HTML file will be created in the same directory as the notebook (or the specified output directory).

PDF Export

Exporting directly to PDF from VS Code requires system-level installation of Pandoc and a LaTeX distribution. For most use cases, exporting to HTML and using your browser's "Print to PDF" is simpler and produces good results.