中文簡介

機器學習（ML）是對計算機系統使用的算法和統計模型的科學研究，這些算法和統計模型不使用顯式指令，而是依靠模式和推理來有效地執行特定的任務。它被視為人工智能的一個子集。機器學習算法建立一個樣本數據的數學模型，稱為“訓練數據”，以便在沒有明確編程來執行任務的情況下做出預測或決策。機器學習算法被廣泛應用于各種各樣的應用中，如電子郵件過濾和計算機視覺，在這些應用中，它對數據是不可行的。執行任務的特定指令的算法。機器學習與計算統計密切相關，計算統計集中于使用計算機進行預測。數學優化的研究為機器學習領域提供了方法、理論和應用領域。數據挖掘是機器學習中的一個研究領域，其重點是通過無監督學習進行探索性數據分析在其跨業務問題的應用中，機器學習也稱為預測分析。

　　英文簡介

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.

　　近年期刊自引率趨勢圖

　　JCR分區

JCR分區等級	JCR所屬學科	分區	影響因子
Q2	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	Q2	5.414

　　近年期刊影響因子趨勢圖

　　CiteScore數值

CiteScore	SJR	SNIP	學科類別	分區	排名	百分位
7.20	1.640	2.348	大類：Computer Science 小類：Software	Q1	81 / 398	79%
			大類：Computer Science 小類：Artificial Intelligence	Q1	65 / 269	76%

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