機器學習的基礎概念:
Machine learning is a branch of AI that teaches computers to learn from data and improve with experience, without being explicitly programmed.
Machine Learning(Wikipedia)
What is machine learning?(Machine learning, explained | MIT Sloan)
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
The goal of AI is to create computer models that exhibit “intelligent behaviors” like humans, according to Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world.
Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as “the field of study that gives computers the ability to learn without explicitly being programmed.”
15種經典機器學習算法[轉]
訓練方式 | |
---|---|
線性迴歸 | 監督學習 |
邏輯迴歸 | 監督學習 |
線性判別分析 | 監督學習 |
決策樹 | 監督學習 |
樸素貝葉斯 | 監督學習 |
K鄰近 | 監督學習 |
學習向量量化 | 監督學習 |
支持向量機 | 監督學習 |
隨機森林 | 監督學習 |
AdaBoost | 監督學習 |
高斯混合模型 | 非監督學習 |
限制波爾茲曼機 | 非監督學習 |
K-means 聚類 | 非監督學習 |
最大期望算法 | 非監督學習 |