Computational Learning Tasks for Predictions
Computational learning tasks involve creating models that can make predictions based on data. These tasks are a core aspect of machine learning and artificial intelligence, where the goal is to learn patterns from historical data to make accurate predictions about new, unseen data.
Types of Learning Tasks:
- Supervised Learning:
- Regression: Predicting continuous values (e.g., predicting house prices).
- Classification: Predicting categorical labels (e.g., spam detection in emails).
- Unsupervised Learning:
- Clustering: Grouping similar data points (e.g., customer segmentation).
- Association: Finding rules that describe large portions of data (e.g., market basket analysis).
- Semi-Supervised Learning: Combines a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy.
- Reinforcement Learning: Learning to make sequences of decisions by receiving rewards or penalties (e.g., training a robot to navigate a maze).
Learning as Function Approximation
In many machine learning tasks, learning can be viewed as the problem of approximating an unknown function $f$ that maps inputs $X$ to outputs $Y$.
Key Concepts:
- Input Space (\(X\)): The set of all possible inputs.
- Output Space (\(Y\)): The set of all possible outputs.
- Target Function (\(f\)): The true function we aim to approximate.
- Hypothesis Space (\(\mathcal{H}\)): The set of all functions that the learning algorithm can choose from to approximate \( f \).
- Learning Algorithm: A procedure that selects a function \( \hat{f} \in \mathcal{H} \) based on training data.
Example:
In a regression task, we might model the relationship between years of experience (X) and salary (\(Y\)). The goal is to learn a function \( \hat{f}(X) \) that predicts the salary for a given level of experience.
Generalization Concept
Generalization refers to the ability of a learned model to perform well on new, unseen data, not just the training data. It is a critical aspect of machine learning because the ultimate goal is to apply the model to make predictions on new data.
Key Concepts: