Personalized Machine Learning Code Workbooks
This repo contains official code workbooks for textbook Personalized Machine Learning, about following topics:
| Chapter | Topics | Implemented (or Used) Models |
|---|---|---|
| 2 | Regression and Feature Engineering | Linear Regression (sklearn) |
| 3 | Classification and the Learning Pipeline | Linear Regression (TF, PyTorch), Logistic Regression (TF, PyTorch), Ridge Model (sklearn) |
| 4 | Introduction to Recommender Systems | Similarity-based Recommendation Models |
| 5 | Model-based Approaches to Recommendation | Latent Factor Model (surpirse, TF, PyTorch), BPR (implicit, TF, PyTorch) |
| 6 | Content and Structure in Recommender Systems | Factorization Machine (pyFM), BPR (TF, PyTorch) |
| 7 | Temporal and Sequential Models | AutoRegression (sklearn), MF (TF, PyTorch), FMC(TF, PyTorch), FPMC (TF, PyTorch) |
| 8 | Personalized Models of Text | BoW, N-Gram, TF-IDF, Ridge Model (sklearn), word2vec (gensim), item2vec (gensim), |
| 9 | Personalized Models of Visual Data | Visual Compatibility Model (TF, PyTorch), |
| 10 | The Consequences of Personalized Machine Learning | Fairness-Aware Latent Factor Model (TF, PyTorch), BPR (implicit) |
For each chapter, we include a requirements.txt file and a jupyter notebook Chapter_*.ipynb. To run the code in notebook of chapter i, you need:
- open folder
Chapter_i; - use
pip install -r requirements.txtto install the packages; - run code in
Chapter_i.ipynb, data will be saved inChapter_i/datafolder.
Please submit an issue or send an email to [email protected], if you have any questions or suggestions.