machine learning编辑历史

# 1 Foundational Concepts

## 1.1 Definition and Scope

Machine learning is a field of artificial intelligence that endows computers with the ability to learn from data without being explicitly programmed for every possible situation. The core idea is that a system can automatically improve its performance on a task by processing examples, identifying patterns, and generalizing from them. The scope of machine learning ranges from simple linear models used in statistics to complex deep neural networks that process images, text, and speech. All machine learning methods share the fundamental goal of creating models that capture the underlying structure of data to make accurate predictions or decisions on new, unseen inputs.

## 1.2 Relation to Artificial Intelligence and Data Science

Machine learning sits at the intersection of artificial intelligence (AI) and data science. AI is the broader discipline concerned with creating systems that exhibit intelligent behavior, while machine learning provides the core techniques for achieving many AI capabilities, such as perception, reasoning, and decision-making. Data science, in turn, focuses on extracting knowledge and insights from data; machine learning serves as one of its primary analytical tools, enabling predictive modeling, clustering, and anomaly detection. Thus, machine learning acts as a bridge between the statistical analysis of data and the goal of building autonomous intelligent systems.

## 1.3 Types of Learning

### 1.3.1 Supervised Learning

In supervised learning, the algorithm is provided with a labeled dataset, meaning each training example is paired with an output label. The task is to learn a mapping from inputs to outputs so that the model can predict the correct label for new, unseen inputs. Supervised learning is used for both regression (predicting continuous values) and classification (predicting discrete categories). Common examples include spam detection, house price prediction, and medical diagn