机器学习的未来在于开发能够从更少的例子中学习并更有效地进行归纳的算法。
The design of learning algorithms should be guided by both theoretical insights and practical considerations.
学习算法的设计应同时受到理论见解和实践考虑的指导。
In the context of computational learning, the concept of 'probably approximately correct' (PAC) learning provides a framework for understanding the efficiency and feasibility of learning algorithms.
在计算学习的背景下，“可能近似正确”（PAC）学习的概念为理解学习算法的效率和可行性提供了一个框架。
The success of a learning algorithm depends on its ability to balance between fitting the data and avoiding overfitting.
学习算法的成功取决于其在拟合数据和避免过拟合之间取得平衡的能力。
Learning is not just about finding patterns, but about understanding the underlying mechanisms that generate those patterns.
学习不仅仅是寻找模式，而是理解生成这些模式的潜在机制。
A good learning algorithm should be able to handle noise and uncertainty in the data effectively.
一个好的学习算法应该能够有效地处理数据中的噪声和不确定性。
The challenge in learning is not just to memorize, but to generalize from specific examples to broader concepts.
学习的挑战不仅在于记忆，还在于从具体例子中归纳出更广泛的概念。
In computational learning theory, we seek to understand the fundamental principles that govern learning from data.
在计算学习理论中，我们试图理解从数据中学习的基本原理。
The ultimate goal of machine learning is to make computers learn from experience and improve their performance 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性能。
The pursuit of knowledge in computer science is a journey through the landscape of logic and creativity.
计算机科学中的知识追求是一次穿越逻辑和创造力的旅程。
The real test of an algorithm is not just its correctness, but its scalability and robustness.