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Machine Learning Practical Course Code Repository

mlpractical 최신버젼 다운로드

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# Machine Learning Practical

This repository contains the course materials for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course [Machine Learning Practical](http://www.inf.ed.ac.uk/teaching/courses/mlp/).

This assignment-based course focuses on the implementation and evaluation of machine learning systems. Students will gain hands-on experience in designing, implementing, training, and evaluating machine learning systems through practical exercises and assignments.

## Repository Structure

The code in this repository is organized as follows:

* **`mlp` package**: A [NumPy](http://www.numpy.org/)-based neural network library designed specifically for this course. Students will implement parts of this package and extend it during labs and assignments.
* **`notebooks` directory**: A collection of [Jupyter](http://jupyter.org/) notebooks containing explanatory material and hands-on coding exercises for the course labs.

## Remote Working

If you are working remotely, please follow the instructions in our [remote working guide](notes/remote-working-guide.md).

## Getting Started

### Environment Setup

Before you can work with the course materials, you'll need to set up your Python development environment. This includes:

- Installing Python and required packages (NumPy, Jupyter, matplotlib, etc.)
- Setting up the course repository
- Configuring Jupyter notebooks
- Verifying your installation

**📋 Complete setup instructions**: Please follow the step-by-step guide in [`notes/environment-set-up.md`](notes/environment-set-up.md). This guide covers installation for different operating systems and provides troubleshooting tips.

All students will configure their environment during the first lab session with assistance from teaching staff.

## Working with the Exercises

**New to Jupyter notebooks?** Start with `notebooks/00_notebook.ipynb` to familiarize yourself with the Jupyter interface and features.

**Getting started with lab exercises:**
1. Navigate to the `notebooks` directory
2. For lab 1, work with notebooks starting with prefix `01`
3. Continue with subsequent numbered notebooks for each lab session

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