Statistical learning theory provides a mathematical foundation for understanding the performance of different learning algorithms and models. It considers the trade-offs between model complexity and the amount of data available for learning, and it provides theoretical guarantees on the performance of different learning algorithms in terms of their ability to generalize to new data.
The development of statistical learning theory before the recent rise of machine learning is closely tied to the evolution of statistical theory, pattern recognition, and early efforts at automated decision-making. The goal was to understand and formalize the principles behind making predictions and decisions based on data.
Machine learning, on the other hand, is concerned with the practical application of statistical learning theory. It involves the development and implementation of algorithms and models that can learn from data and make predictions or decisions based on that data. Machine learning algorithms can be seen as practical realizations of the theoretical frameworks provided by statistical learning theory.
In this sense, machine learning can be viewed as a practical approach to statistical learning theory, with the goal of building systems that can learn and make predictions from data in real-world applications. The use of machine learning algorithms and models is often driven by the need for practical solutions to real-world problems, while statistical learning theory provides a rigorous foundation for understanding the underlying principles of the learning process.
You can think of it this way: statistical learning theory helps us understand and formalize the properties and behaviors of learning algorithms, while machine learning focuses on the development, implementation, and application of these algorithms.
As the preliminary part, a concise introduction to Linear will be provided, which will help novices read further to the following main part. Essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. If you want to see the applied approach to statistical learning, see Theory of Machine Learning
the mathematical subfield of machine learning whose practitioners aim to elucidate the fundamental principles that explain why/when models trained on empirical data can/will generalize to unseen data. One of the primary aims for several decades of statistical learning researchers has been to bound the generalization gap, relating the properties of the model class, the number of samples in the dataset.
While there is no substitute for a proper introduction to statistical learning theory (see Boucheron et al. (2005), Vapnik (1998)), I will give you just enough intuition to get going. We will revisit generalization, exploring both what is known about the principles underlying generalization in various models, and also heuristic techniques that have been found (empirically) o yield improved generalization on tasks of practical interest.