https://github.com/kons-5/ist-aaut
This repository contains lab materials for the IST-AAut course.
https://github.com/kons-5/ist-aaut
arx-model cnn-classification jupyter-notebooks machine-learning machine-learning-algorithms optuna outlier-removal pytorch pytorch-lightning regression-models segmentation-models sklearn statsmodels
Last synced: about 2 months ago
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This repository contains lab materials for the IST-AAut course.
- Host: GitHub
- URL: https://github.com/kons-5/ist-aaut
- Owner: Kons-5
- Created: 2024-09-17T11:41:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-12T17:30:13.000Z (over 1 year ago)
- Last Synced: 2025-01-15T08:59:59.093Z (over 1 year ago)
- Topics: arx-model, cnn-classification, jupyter-notebooks, machine-learning, machine-learning-algorithms, optuna, outlier-removal, pytorch, pytorch-lightning, regression-models, segmentation-models, sklearn, statsmodels
- Language: Jupyter Notebook
- Homepage:
- Size: 110 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IST-AAut
This repository contains lab materials for the IST-AAut (Machine Learning) course.
- Check [Report.pdf](Submissions/Report.pdf) for the methodology and analysis, with comparisons of models across different metrics.
## Content
### [Part 1: Regression with Synthetic Data](AAutLab2425.pdf)
This section covers regression analysis tasks using synthetic data to explore key concepts in predictive modeling.
- [ML-Submission1](Submissions/ML-Submission1): Contains the Jupyter Notebook for **Multiple Linear Regression with Outliers**, implementing outlier removal, cross-validation, and tuning techniques as described in the report.
- [ML-Submission2](Submissions/ML-Submission2): Contains the Jupyter Notebook for the **ARX Model**, focusing on time-series data and system response modeling, with parameter optimization techniques.
#### Learning Objectives
- Understand multiple linear regression with synthetic data containing noise and outliers.
- Apply ARX (Auto-Regressive with eXogenous input) models for time-series data analysis.
- Evaluate model robustness with cross-validation and tuning techniques.
#### Technologies
- Python (3.11) with `scikit-learn` and `statsmodels`.
- MATLAB® for additional analysis and model validation.
### [Part 2: Image Analysis - Martian Crater Detection](AAutLab2425.pdf)
This section explores image classification and segmentation tasks focused on low-resolution (48x48) Martian crater analysis.
- [ML-Submission3](Submissions/ML-Submission3): Contains the Jupyter Notebook for **Image Classification** using SVC and CNN models, with techniques for handling imbalanced data and data augmentation.
- [ML-Submission4](Submissions/ML-Submission4): Contains the Jupyter Notebook for **Image Segmentation**, implementing MLP-Fusion and U-Net models for pixel-wise segmentation.
#### Learning Objectives
- Develop machine learning models to classify crater vs. non-crater images.
- Apply segmentation techniques (patch-based and pixel-based) to delineate crater boundaries.
- Address data imbalance with techniques like SMOTE and data augmentation.
#### Technologies
- Python (3.11) with `torch`, `torchvision`, `torchmetrics`, and `pytorch-lightning`.
- [Optuna](https://optuna.org/) for hyperparameter tuning.
## Authors
- [João Gonçalves - sqrt(-1)](https://github.com/eusouojoao)
- [Teresa Nogueira - 13A!](https://github.com/FrolickingAsteroid)
## License
This work is licensed under a [Creative Commons Attribution Non Commercial Share Alike 4.0 International][cc-by-nc-sa].
[cc-by-nc-sa]: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode