偏差和方差之间的权衡是机器学习中的一个基本概念。
The success of a learning algorithm depends on the quality and quantity of the data it is trained on.
学习算法的成功取决于其训练数据的质量和数量。
The study of learning algorithms must consider both the statistical and computational aspects of learning.
学习算法的研究必须同时考虑学习的统计和计算方面。
The concept of computational efficiency is central to the design of learning algorithms.
计算效率的概念是学习算法设计的核心。
The PAC learning framework provides a formal way to quantify the learnability of a concept class.
PAC学习框架提供了一种形式化的方法来量化概念类的可学习性。
A key insight in learning theory is that the complexity of a hypothesis class is crucial for generalization.
学习理论中的一个关键见解是，假设类的复杂性对于泛化至关重要。
The challenge in computational learning theory is to understand the capabilities and limitations of learning algorithms.
计算学习理论中的挑战在于理解学习算法的能力和局限性。
The ultimate goal of machine learning is to make computers learn from experience and improve their performance on tasks over time.
机器学习的最终目标是让计算机从经验中学习，并随着时间的推移提高其在任务上的表现。
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