🌐 Domain Adaptation for Machine Learning

Advanced domain adaptation techniques for robust machine learning across different data distributions

🎯 Project Overview

This project implements domain adaptation methods to address the challenge of distribution shift in machine learning. When models trained on one domain (source) need to perform well on another domain (target), domain adaptation techniques bridge the gap between different data distributions.

🔑 Key Features

• ✅ Distribution Alignment techniques for domain shift
• ✅ Transfer Learning implementations
• ✅ Adversarial Domain Adaptation methods
• ✅ Performance Evaluation across domains
• ✅ Real-world Applications with practical datasets

🧠 Domain Adaptation Framework

Core Challenges Addressed

• Covariate Shift - Feature distribution changes
• Label Shift - Class distribution changes
• Concept Drift - Decision boundary changes
• Domain Gap - Systematic differences between domains

Methods Implemented

• Statistical Alignment - Distribution matching techniques
• Adversarial Training - Domain-adversarial neural networks
• Feature Transformation - Domain-invariant representations
• Instance Weighting - Sample importance reweighting

📊 Implementation Highlights

Python Architecture

# Core domain adaptation pipeline
# Source and target domain processing
# Feature alignment algorithms
# Transfer learning optimization
# Performance evaluation metrics

Key Algorithms

• Domain-Adversarial Neural Networks (DANN)
• Maximum Mean Discrepancy (MMD) alignment
• Coral (Correlation Alignment)
• Gradient Reversal Layer implementation

🛠️ Technical Stack

• Language: Python 3.8+
• ML Frameworks: scikit-learn, PyTorch/TensorFlow
• Data Processing: pandas, numpy
• Visualization: matplotlib, seaborn
• Evaluation: Custom domain adaptation metrics

📁 Project Structure

Domain-Adaptation-ML/
├── domain_adaptation_main.py              # Main implementation
├── domain_adaptation_report.pdf           # Detailed research report
├── requirements.txt                       # Dependencies
├── LICENSE                                # MIT License
├── README.md                             # This file
└── results/                              # Analysis outputs
    ├── adaptation_performance.csv        # Cross-domain results
    ├── domain_alignment_plots.png        # Visualization outputs
    └── transfer_learning_metrics.json    # Evaluation metrics

📈 Applications & Use Cases

Industry Applications

• Healthcare - Medical imaging across hospitals/devices
• Finance - Credit scoring across different populations
• NLP - Sentiment analysis across languages/domains
• Computer Vision - Object detection across environments

Research Impact

• Robust model deployment across domains
• Reduced data collection costs
• Improved model generalization
• Cross-domain knowledge transfer

🎓 Academic Context

This work demonstrates expertise in:

• Advanced Machine Learning - Distribution shift handling
• Transfer Learning - Cross-domain knowledge transfer
• Statistical Learning Theory - Domain adaptation theory
• Practical ML Engineering - Real-world deployment challenges

Educational Background:

• Master's MIASHS/AI - Université Lyon 2 (2024-2025)
• Master's Statistics - Université de Neuchâtel (2021-2023)
• Strong foundation in statistical learning and ML theory

🚀 Getting Started

git clone https://github.com/OJules/Domain-Adaptation-ML.git
cd Domain-Adaptation-ML
pip install -r requirements.txt
python domain_adaptation_main.py

📚 References & Theory

• Ganin, Y., et al. (2016). Domain-adversarial training of neural networks
• Long, M., et al. (2015). Learning transferable features with deep adaptation networks
• Sun, B., et al. (2016). Correlation alignment for unsupervised domain adaptation

🤝 Collaboration Opportunities

This project is valuable for:

• Industry applications requiring robust ML deployment
• Academic research in transfer learning
• Consulting projects with domain shift challenges
• Open source contributions to domain adaptation

📫 Contact

Jules Odje - Data Scientist | Aspiring PhD Researcher
📧 odjejulesgeraud@gmail.com
🔗 LinkedIn
🐙 GitHub

Expertise Areas: Domain Adaptation | Transfer Learning | Statistical ML | Robust AI

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"Bridging the gap between domains for robust and generalizable machine learning"