This repository hosts the coursework project for EE 531 from the Indian Institute of Technology Mandi, focusing on estimation and detection. The primary objective of this project is to develop a custom classifier and implement a Likelihood Ratio Test (LRT) for the same.
In this project, we aim to design and implement a classifier tailored to specific requirements, leveraging concepts taught in the EE 531 course. The classifier will be developed using relevant algorithms and methodologies covered in the course curriculum. Additionally, we will establish a Likelihood Ratio Test (LRT) framework to evaluate the performance of our classifier.
- Develop a custom classifier for the given problem domain.
- Implement a Likelihood Ratio Test (LRT) to assess the classifier's performance.
- Document the process, methodologies, and outcomes comprehensively.
- Use the EEG data provided by data , visualize and distinguish between our null and alternative hypothesis
- Clone the repository to your local machine.
- Navigate to the project directory.
- Follow the instructions provided in the project documentation for setting up and running the classifier and LRT.
- Provide any necessary input data or parameters as instructed.
- Analyze the results obtained from the classifier and LRT implementation.
We would like to express our gratitude to the faculty and staff of IIT Mandi for their guidance and support throughout this project.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for academic and non-commercial purposes. Refer to the License file for more details.
For any inquiries or feedback regarding the project, please reach out to [abhishekdileep99@email.com].