📅 2025-02-27 — Session: Developed Autoencoder-based Socioeconomic Embeddings
🕒 02:55–03:35
🏷️ Labels: Autoencoder, Feature Selection, Socioeconomic Data, Machine Learning, Embedding
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and develop methodologies for creating efficient socioeconomic embeddings using advanced machine learning techniques, particularly autoencoders.
Key Activities
- Reviewed the framework for a data science graduate thesis, focusing on representation learning with autoencoders.
- Planned a methodology for training a Random Forest model on autoencoder-generated embeddings.
- Outlined an iterative approach for enhancing embeddings using census and survey data.
- Discussed strategies for optimizing the order of feature addition in autoencoders.
- Reflected on feature selection in deep learning for structured data.
- Finalized a feature subset for socioeconomic embeddings.
Achievements
- Established a structured approach for maximizing the impact of a data science thesis using advanced techniques.
- Developed a plan for integrating Random Forest models with autoencoder embeddings to handle high-dimensional data.
- Created a strategy for iterative embedding expansion using real-world data sources.
- Finalized a stable feature subset for effective socioeconomic trait encoding.
Pending Tasks
- Implement the outlined methodologies and validate their effectiveness with real-world data.