Difference between revisions of "Learning Techniques"
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Inference refers to reaching an outcome or decision. In machine learning, fitting a model and making a prediction are both types of inference. There are different paradigms for inference that may be used as a framework for understanding how some machine learning algorithms work or how some learning problems may be approached. | Inference refers to reaching an outcome or decision. In machine learning, fitting a model and making a prediction are both types of inference. There are different paradigms for inference that may be used as a framework for understanding how some machine learning algorithms work or how some learning problems may be approached. | ||
* Inductive Learning | * Inductive Learning | ||
Revision as of 13:03, 8 December 2019
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- 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery
- Natural Language
Learning Problems:
Hybrid Learning Problems:
Statistical Inference:
Inference refers to reaching an outcome or decision. In machine learning, fitting a model and making a prediction are both types of inference. There are different paradigms for inference that may be used as a framework for understanding how some machine learning algorithms work or how some learning problems may be approached.
- Inductive Learning
- Deductive Inference
- Transductive Learning
Learning Techniques:
- Active Learning
- Online Learning
- Text Transfer Learning
- Image/Video Transfer Learning
- Few Shot Learning
- Transfer Learning a model trained on one task is re-purposed on a second related task
- Ensemble Learning
- Multi-Task Learning (MTL)
- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Imitation Learning
- Simulated Environment Learning
- Lifelong Learning - Catastrophic Forgetting Challenge
