Predictive Analytics for Text
Model Understanding and Generative Alignment Laboratory (MUGA LAB)
Academic Year 2025–2026
Overview
Predictive Analytics for Text is a modular course exploring supervised learning for text data,
with a focus on interpretability, reproducibility, and uncertainty estimation.
Students will build pipelines for:
- Preprocessing and vectorizing textual data
- Training classification and regression models
- Analyzing model behavior using SHAP and calibration
- Incorporating BayesFlow for uncertainty and probabilistic inference
Learning Objectives
By the end of this course, students will be able to:
| Objective | Description |
|---|---|
| Text Preprocessing | Perform tokenization, lemmatization, and normalization using spaCy |
| Vectorization | Implement Bag-of-Words, TF–IDF, and embedding-based models |
| Supervised Learning | Train and evaluate classification and regression pipelines |
| Feature Analysis | Apply SHAP, ECE, and calibration metrics for interpretability |
| Deep Architectures | Build MLPs, GRUs, LSTMs, and Transformers for text |
| Uncertainty Modeling | Integrate BayesFlow for predictive uncertainty |
| Optimization | Use Optuna and DEHB for hyperparameter search |
| Communication | Present interpretable results through scientific reporting |
Course Schedule
| Week | Topics | Models / Methods |
|---|---|---|
| 1 | Text preprocessing | spaCy, tokenization, lemmatization |
| 2 | Traditional vectorization | BoW, TF–IDF |
| 3 | Word embeddings | Word2Vec, FastText, GloVe |
| 4 | Classification I | Perceptron, Logistic Regression, Naive Bayes |
| 5 | Classification II | SVM, Random Forest, XGBoost |
| 6 | Regression | Linear, SVR, Random Forest, XGBoost |
| 7 | Evaluation and metrics | F1, ROC AUC, MSE, MAE, R² |
| 8 | Feature importance | SHAP, Optuna visualization |
| 9 | Deep models I | MLP, RNN-based |
| 10 | Deep models II | LSTM, GRU |
| 11 | Transformers and TabNet | Attention and interpretability |
| 12 | BayesFlow for Uncertainty | Bayesian inference and calibration |
| 13–15 | Capstone Project | Final model, report, and presentation |
Tools and Frameworks
| Domain | Libraries |
|---|---|
| Text Processing | spaCy, NLTK, pandas, NumPy |
| ML & Evaluation | scikit-learn, XGBoost, Optuna |
| Deep Learning | PyTorch, transformers, torchtext, TabNet |
| Interpretability & Uncertainty | SHAP, ECE, BayesFlow, matplotlib |
Assessment Structure
| Component | Weight |
|---|---|
| Chapter Critiques (6 total) | 30% |
| Long Tests (2) | 30% |
| Capstone Project | 30% |
| Assignments | 10% |
Capstone Project
Goal: Develop a complete text prediction pipeline with interpretability and uncertainty components.
Requirements:
- At least one deep architecture (e.g., GRU, Transformer, TabNet)
- Incorporation of BayesFlow or other probabilistic model
- Comprehensive interpretability analysis (e.g., SHAP + calibration curves)
Deliverables:
- Technical report (5–7 pages)
- Oral presentation
- Reproducible code and artifacts
Integration with MUGA LAB Framework
This course follows the MUGA LAB Pedagogical Stack, linking interpretability and alignment through structured pedagogy.
| Layer | Description |
|---|---|
| Mathematical Foundation | Optimization, calibration, uncertainty |
| Computational Framework | Reproducible pipelines (TF–IDF, embeddings, SHAP, DEHB) |
| Pedagogical Layer | Structured prompt-based learning |
| Research Integration | Connected to Model Understanding and Generative Alignment (2025) |
Course Materials
| Module | Description | Location |
|---|---|---|
| Traditional Vectorization | TF–IDF and Bag-of-Words workflows | /notebooks/traditional_vectorization.ipynb |
| Embedding Vectorization | Word2Vec, FastText, GloVe, BayesFlow | /notebooks/embedding_vectorization.ipynb |
| Master Split Generation | Reproducible train/val/test split indices | /scripts/generate_master_split.py |
| Helper Modules | Preprocessing and indexing utilities | /helpers/ |
References
-
MUGA LAB (2025) — Model Understanding and Generative Alignment
Read Reference PDF -
Additional readings and papers will be announced throughout the course.
✉️ Contact
📧 MUGA LAB — mugalab.research@gmail.com
🌐 https://lexmuga.github.io/mugalab