In this module, you will explore cutting-edge developments in machine learning and artificial intelligence, learning how these technologies are applied to solve challenging problems. The goal is to provide you with a comprehensive understanding of the capabilities of various machine learning techniques, their theoretical underpinnings, potential pitfalls, and practical implementation for problem-solving.
By the end of this module, you will:
- Gain knowledge of current machine learning techniques and their applications.
- Understand the theory behind popular methods such as:
- Random Forests
- Support Vector Machines
- Convolutional Neural Networks
- Generative Adversarial Networks
- Develop skills to implement these techniques in practical projects.
This module is structured over 12 weeks, combining theoretical lectures with hands-on computer lab projects. You will apply machine learning methods to solve complex problems in physics and related fields, providing you with a robust foundation in AI and statistical data analysis.
- Clone this repository to your local machine:
git clone https://github.com/yourusername/DAT6501-AI-and-Statistical-Data-Analysis-2024-25.git
Feel free to submit issues or pull requests if you have suggestions or improvements for the repository.
This project is licensed under the MIT License.