https://github.com/quantum-software-development/gaussian-processes-bayesian-optimization-
📈 A comprehensive course on Gaussian Processes and Bayesian Optimization, covering theory, implementation, and real-world applications.
https://github.com/quantum-software-development/gaussian-processes-bayesian-optimization-
artificial-intelligence bayesianoptimization data-science gaussianprocesses jupyter-notebook machine-learning optimization python3 research statistics
Last synced: 2 months ago
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📈 A comprehensive course on Gaussian Processes and Bayesian Optimization, covering theory, implementation, and real-world applications.
- Host: GitHub
- URL: https://github.com/quantum-software-development/gaussian-processes-bayesian-optimization-
- Owner: Quantum-Software-Development
- License: mit
- Created: 2024-11-05T20:25:46.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-05T19:51:24.000Z (3 months ago)
- Last Synced: 2025-03-05T20:52:25.707Z (3 months ago)
- Topics: artificial-intelligence, bayesianoptimization, data-science, gaussianprocesses, jupyter-notebook, machine-learning, optimization, python3, research, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 1.83 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
#
📈 Gaussian Processes and Bayesian Optimization Course
Welcome to the repository for the Gaussian Processes and Bayesian Optimization course! This course is designed to provide a comprehensive understanding of Gaussian Processes (GPs) and their application in Bayesian Optimization (BO).
https://github.com/user-attachments/assets/eff405d3-fc72-499f-98e2-0e198c37b8b4
###
[](https://github.com/sponsors/Quantum-Software-Development)
## Introduction
### What are Gaussian Processes?
Gaussian Processes are a powerful and flexible statistical tool used for regression and classification tasks. They provide a probabilistic framework for modeling complex functions by defining a distribution over functions rather than fitting a single function. This allows us to quantify uncertainty in our predictions. GPs are particularly useful when dealing with small datasets where traditional methods may struggle.
### What is Bayesian Optimization?
Bayesian Optimization is a strategy for finding the minimum or maximum of an expensive function that is often noisy or has unknown gradients. It utilizes Gaussian Processes as a surrogate model to guide the search for the optimal solution. By iteratively refining this model and focusing on areas of the input space that are likely to yield better results, Bayesian Optimization is particularly effective for optimizing hyperparameters in machine learning models, tuning engineering designs, and other real-world applications where evaluating the objective function is costly.
## Table of Contents
- [Course Overview](#course-overview)
- [Prerequisites](#prerequisites)
- [Course Structure](#course-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Resources](#resources)
- [Contributing](#contributing)
- [License](#license)
## Course Overview
In this course, you will learn:
- The fundamentals of Gaussian Processes, including kernels, hyperparameters, and inference.
- How to implement Bayesian Optimization using GPs as a surrogate model.
- Practical applications of GPs and BO in real-world problems.
By the end of the course, you will have hands-on experience with GPs and the ability to apply Bayesian Optimization techniques to optimize complex functions.
## Prerequisites
- Basic knowledge of Python programming.
- Familiarity with machine learning concepts.
- Understanding of probability and statistics will be beneficial.
## Course Structure
The course consists of the following modules:
1. Introduction to Gaussian Processes
2. Gaussian Process Regression
3. Kernel Functions and Hyperparameter Tuning
4. Bayesian Optimization Fundamentals
5. Implementing Bayesian Optimization
6. Advanced Topics and Applications
## Installation
To get started, clone this repository and install the required packages using pip:
```bash
git clone https://github.com/yourusername/gaussian-processes-bayesian-optimization.git
```
```bash
cd gaussian-processes-bayesian-optimization
```
```bash
pip install -r requirements.txt
```
## Requirementes
### Library Descriptions:
- numpy: For numerical calculations and array manipulation.
- scipy: For scientific functions and advanced computation.
- pandas: For data manipulation and analysis.
- matplotlib: For data visualization.
- seaborn: For statistical visualization.
- scikit-learn: For machine learning algorithms and useful tools.
- gpytorch: For working with Gaussian Processes.
- bayesian-optimization: For implementing Bayesian optimization techniques.
- jupyter: For running Jupyter notebooks, which are useful for course presentations
## How to Create the requirements.txt File
To create the `requirements.txt`b file, you can simply copy the dependencies above and paste them into a file named`requirements.txt` in the root of your project.
If you are using a virtual environment (like virtualenv or conda), you can also automatically generate a `requirements.txt` file with the installed packages using:
```bash
pip freeze > requirements.txt
```
## Usage
```bash
pip show gpytorch
pip show bayesian-optimization
pip show jupyter
```
```bash
jupyter notebook
```
## Resources
- [Gaussian Processes for Machine Learning](book)http://www.gaussianprocess.org/gpml/)
- [Bayesian Optimization in Python](https://github.com/fmfn/BayesianOptimization)
## Contributing
Contributions to enhance this course material are welcome! Please submit a pull request or open an issue for any suggestions or improvements.