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Learning Experience \(E\)

Learning Paradigms

Method Meaning Application
Supervised Uses labelled data, to derive a mapping between input examples and target variable. \(D_\text{train} = X, y\)
Unsupervised Learning from unlabelled data \(D_\text{train} = X\)
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
Self-Supervised Supervised learning without explicit labels; labels created from the data Images: identify correct rotations
Sequences: out-of-sequence corrections
Text: word embeddings
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.
\(D_\text{train} = X, \text{Feedback}\) 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
Online Streaming

Training Method

Advantage
Batch \(\hat f: X \to y\) Better model
Streaming/
Online/
Passive-Aggressive
\(\hat f_b: X_{i \le b} \to y_{i \le b}\)
where \(b= \text{Mini-batch}\)
- Adaptive to new data points
- Computationally-cheap
Hybrid - Batch training start of day
- Online training intra-day

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
Last Updated: 2024-12-26 ; Contributors: AhmedThahir, web-flow

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