Chapter 1 Machine Learning Overview
machine learning concept
Learning: Learning can be explained from different perspectives, but all include the two main aspects of knowledge acquisition and ability improvement. Therefore, the general explanation for learning is as follows: learning is a process of knowledge acquisition and ability growth with a specific purpose. Its internal behavior is to acquire knowledge, accumulate experience and discover laws, etc., and its external performance is to improve performance, adapt to the environment, and achieve self-improvement.
Machine learning: Machine learning is a subject that studies how to use computers to simulate or realize human learning activities. It is the intersection of computer science, mathematics, psychology, biology and philosophy.
The history of machine learning
The first stage is the warm period from the mid-1950s to the mid-1960s. The second phase was the cool-off period known as machine learning from the mid-1960s to the mid-1970s. The third phase from the mid-1970s to the mid-1980s is called the Renaissance. The fourth stage, from the mid-1980s to the present, is the latest stage of machine learning.
Classification of Machine Learning
Classification based on learning strategies: machine learning that simulates the human brain, including symbolic learning and neural network learning; machine learning that directly adopts mathematical methods.
Classification based on learning methods: Inductive learning: including symbolic inductive learning and functional inductive learning; deductive learning; analogical learning; analytical learning.
Classification based on learning styles: supervised learning; unsupervised learning; reinforcement learning.
Classification based on data form: structured and unstructured learning.
Learning Objective-Based Separation: Concept Learning; Rule Learning; Function Learning; Category Learning; Bayesian Network Learning.
Main Strategies of Machine Learning
1. Learning based on neural network