Table 4.
Machine learning's topic prior (φ z) and word distributions (φ e,z) of its related entities.
| Machine learning | Judea Pearl | Pedro Domingos | Andrew Ng |
|---|---|---|---|
| Learning | Causal | Logic | Learning |
| Machine | Revisited | Markov | Stanford |
| Data | Markovian | Networks | Neural |
| Algorithm | Data | Learning | Deep |
| Representation | Counterfactual | MLNs | Networks |
| Theory | Artificial | Algorithm | Word |
| Examples | Explanations | Theory | Technology |
| Sparse | Independence | Knowledge | Model |
| Analysis | Path | Order | |
| Computational | Specificity | Models | Class |
| Artificial | Representation | Systems | Machine |
| Recognition | Proven | Representation | Artificial |
| Programming | Tags | Reasoning | Intelligence |
| Model | Embracing | World | System |
| Supervised | Tolerating | Structure | Data |