Learning Experience \(E\)¶
Goal of Experience¶
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Learning/Inference
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Obtain the Sample CEF which closely matches the Population CEF
or
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Obtain the Sample Conditional Distribution which closely matches the Population Conditional Distribution
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Prediction/Decision
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Making prediction of \(y\) based on the learning
Learning Types¶
| Prediction | Estimation of unseen data |
| Modelling | Characterization How do inputs affect output |
| Optimization | What input values produce desired outputs (both mean and variance) |
| Control | How to adjust controlled inputs to maximize control of outputs |
| Simulation | |
| Causal inference |
Use ML models for developing structural models, and then let the structural models to make the predictions, not the ML models
- Why: Black swans can be predicted by theory, even if they cannot be predicted by ML
- How: Use a non-parametric ML to identify important variables and then develop a parametric structural form model.
Learning Methods¶
| Method | Meaning | Application |
|---|---|---|
| Supervised | Uses labelled data, to derive a mapping between input examples and target variable. | |
| Unsupervised | Learning from unlabelled data | |
| Semi-Supervised | \(\exists\) labelled data and large amount of unlabelled data. Label the unlabelled data using the labelled data. For example, love is labelled as emotion, but lovely isn’t Cotraining, Semi-Supervised SVM | |
| Lazy/Instance-Based | Store the training examples instead of training explicit description of the target function. Output of the learning algorithm for a new instance not only depends on it, but also on its neighbors. The best algorithm is KNN (K-Nearest Neighbor) Algorithm. Useful for recommender system. | |
| Active AL | Learning system is allowed to choose the data from which it learns. There exists a human annotator. Useful for gene expression/cancer classification | |
| Multiple Instance | Weakly supervised learning where training instances are arranged in sets. Each set has a label, but the instances don’t | |
| Transfer | Reuse a pre-trained model as the starting point for a model on a new related task | |
| Reinforcement Learning RL | Learning in realtime, from experience of interacting in the environment, without any fixed input dataset. It is similar to a kid learning from experience. Best algorithm is Q-Learning algorithm. | Game playing |
| Bayesian Learning | Conditional-probabilistic learning tool Each observed training expmle can incrementally inc/dec the estimated probability that a hypothesis is correct. Useful when there is chance of false positive. For eg: Covid +ve | |
| Deep DL | Multi-Layered ANNs | Computer Vision |
| Federated FL | Distributed | Privacy |
Types of Learners¶
They are not adapted by the ML algo itself, but we can use nested learning, where other algorithms optimize the hyperparameter for the ML algo.
| Eager Learner | Lazy Learner | |
|---|---|---|
| Training | Learns relationship between class label & attributes | Stores training records |
| Evaluation | Perform computations to classify evaluation record | |
| Training Speed | Slow | Fast |
| Evaluation Speed | Fast | Slow |
| Example | - Decision Tree - Rule-Based Classifier | - Nearest-neighbor classifier |