1. Learning Theory & Generalization
The principles of learning theory and generalization form the foundation for machine learning, exploring essential questions about model performance on unseen data. Key elements like statistical learning theory, the bias-variance trade-off, and PAC learning are central to understanding how models can effectively learn from limited data while maintaining generalization. The balance between model complexity and performance is emphasized, with various techniques—such as regularization and cross-validation—serving as practical tools for achieving optimal model evaluation and design.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Learning theory provides a foundation for understanding when and how machines can learn.
- Generalization is crucial for machine learning models to perform effectively on unseen data.
- The bias-variance trade-off illustrates the balance required in model complexity to achieve optimal performance.
Key Concepts
- -- Statistical Learning Theory
- A probabilistic framework to understand learning from data.
- -- Generalization
- The ability of a model to perform well on unseen data.
- -- BiasVariance Tradeoff
- A fundamental concept describing the trade-off between error due to bias and error due to variance in a model.
- -- PAC Learning
- A framework that formalizes the conditions under which a concept class can be learned.
- -- VC Dimension
- A measure of the capacity of a hypothesis class based on its ability to classify data points.
- -- Regularization
- A technique to improve generalization by introducing a penalty term in the model's loss function.
- -- Rademacher Complexity
- A measure of a hypothesis class's richness based on its capability to fit random noise.
- -- CrossValidation
- A resampling method used to estimate the performance of machine learning models.
Additional Learning Materials
Supplementary resources to enhance your learning experience.