EEE311: Sport and Machine Learning Project
Introduction
Welcome to the Machine Learning project for the EEE311 course. This semester, our focus is on applying machine learning techniques to sports data. The aim of this project is to give students hands-on experience in building, training, and evaluating machine learning models in the context of sports.
Each student will select a sport-related dataset and apply machine learning algorithms to solve a relevant problem. You can choose from various applications such as performance analysis, injury prediction, game strategy optimization, or player statistics.
Possible Project Topics
Here are some suggested topics to explore for your project:
- Predicting player performance in basketball using past statistics.
- Injury prediction for football players using player data.
- Classifying different types of tennis shots using video or sensor data.
- Predicting match outcomes in soccer using team statistics.
- Using reinforcement learning for optimizing strategies in sports games.
Feel free to propose your own ideas as long as they align with the “Sport and Machine Learning” theme.
Project Deliverables
- Project Proposal:
A document outlining the problem you aim to solve, the dataset you will use, and the machine learning techniques you plan to apply. This will give structure to your project and ensure that your approach is feasible. - Codebase:
The full implementation of your machine learning model, including data preprocessing, model training, and evaluation. Ensure that your code is well-documented and reproducible. - Final Report:
A detailed report explaining your project, the challenges you faced, and an analysis of the results. The report should be thorough, covering your methodology, experimentation, and conclusion. - Presentation:
A 10-15 minute presentation summarizing your project findings. This should include a demo of your model’s performance and insights gained from your analysis.
Project Timeline
| Milestone | Due Date |
|---|---|
| Project Proposal | October 15, 2024 |
| Progress Checkpoint | November 10, 2024 |
| Final Submission | December 31, 2024 |
| Presentations | 6-8-13-15 January, 2025 |
Please adhere to the deadlines to ensure timely feedback and guidance.
Resources
Here are some useful resources to help you with your project:
- Kaggle Datasets – A great source for finding sports-related data.
- Scikit-learn Documentation – For implementing machine learning models.
- TensorFlow – For building deep learning models.
- PyTorch – Another popular framework for deep learning.
Evaluation Criteria
Your project will be evaluated based on:
- Relevance: How well your project aligns with the “Sport and Machine Learning” theme.
- Innovation: Creativity in problem-solving and the application of machine learning techniques.
- Implementation: The quality of your codebase and how well your model performs.
- Analysis: Depth of analysis in your final report, including the results, model performance, and challenges faced.
- Presentation: Clarity and professionalism in presenting your project.
Good luck with your project, and don’t hesitate to reach out if you need any guidance!