计算科学与工程哲学博士-机器学习

机器学习（ML）专注于计算机程序的开发，当遇到新的信息或示例时，这些程序可以自学且无需明确编程即可发挥作用。ML的过程与数据挖掘的过程相似，因为这两个系统都在数据中搜索模式。但是，ML程序不仅可以简单地挖掘供人类解释的数据，还可以使用该数据来重定向或调整自己的操作。ML是人工智能的几个组成部分之一。<br> <br>该领域的研究探索了构建模型并进行数据驱动的预测或决策的算法的构建和研究。它是计算机科学中一个有用的，迅速发展的子领域，几乎所有CSE教师都将其纳入他们的研究。应用范围广泛，包括识别图像，字符和
Artificial intelligence (AI) is the general study of making intelligent machines. Machine learning (ML) is a subtopic of AI that focuses on the development of computer programs that can teach themselves and act/adapt without the need for explicit programming when encountering new information or examples.<br><br>Research in this area explores the construction and study of algorithms that build models and make data-driven predictions or decisions. It is an impactful rapidly evolving sub-field of computer science that nearly all CSE faculty incorporate into their research.<br><br>Work in AI and ML at CSE involves foundational research in deep learning, probabilistic models and reasoning, large-scale machine learning, reinforcement learning, data-driven decision making and AI/ML in science and engineering. There are significant efforts aimed at a wide range of applications, including traditional areas such as recognizing natural language, networks, speech and images, to more cutting-edge novel ones connecting to other domains such as forecasting pandemics like COVID-19, diagnosing and treating complex diseases like asthma and cancer, detecting fraud and online adversarial behavior, designing new materials, AI for social good, incentive design for solving social dilemmas, and predicting the responses of social, natural and engineered systems.