In the realm of machine learning, the statistical learning framework is a pivotal approach that revolves around developing and fine-tuning models based on data. This framework operates on the principle of formulating hypotheses about data patterns and relationships, which are then embodied through models. These models range from simple linear constructs to complex neural networks. The crux of this learning process lies in selecting the most appropriate model from the hypothesis space, which is essentially a collection of potential explanations or patterns that the model can infer from the data.
The fundamental challenge in this framework is to achieve a harmonious balance between the model's complexity and its ability to generalize. Models that are overly complex may fit the training data too closely, a problem known as overfitting, where they fail to perform well on new, unseen data. Conversely, overly simplistic models might miss capturing the underlying data patterns, leading to underfitting. Thus, the statistical learning framework guides the delicate act of model selection, ensuring that the chosen model not only fits the current data but also possesses the robustness to generalize effectively to new datasets.
In this session, we will cover the types of machine learning, statistical learning framework, together with the related terminology and methods, such as overfitting, empirical risk minimization, etc..