Machine learning， as the most important technology and tool in artificial intelligence， has been successfully applied in solving various complex problems. After a brief introduction to the basic methods and algorithms of machine learning， this book collects artificial intelligence solutions for typical complex problems of wide range， such as handwritten digit recognition， radar automatic target recognition， computer-aided disease diagnosis， image filtering for images contaminated with heavy noises， gene expression heterogeneity correction， preeclampsia risk prediction， and some typical combinatorial optimization problems such as multi-constraint shortest path problem， traveling salesman problem， and so forth. The aim is to examine， through these cases， how to use machine learning technology to create effective methods and algorithms for solving complex problems， and which reveals enormous advantages and severe challenges of artificial intelligence technology.
This book can serve as the textbook for undergraduates， graduate students and PhD students for related courses about machine learning and a reference for their research work in the majors of Computer Science， Artificial Intelligence， Automation and so forth in colleges and universities. It can also be a reference for researchers and engineers who are interested in machine learning and artificial intelligence.
机器学习作为人工智能最重要的技术和工具，已成功应用于解决各种复杂问题。本书在简略介绍机器学习的基本方法与算法的基础上，通过搜集典型复杂问题的人工智能解决方案，诸如手写数字识别、雷达自动目标识别、癌症诊断、超强噪声污染情况下的图像过滤、基因芯片异质性校正、孕妇子痫前期风险预测，以及一些典型的组合优化问题，如多约束最短路径问题和旅行商问题等，考察如何运用机器学习技术，创造解决复杂问题的有效方法和算法，并通过这些案例窥视出人工智能技术的巨大优势和其面临的极其严峻的挑战。
本书可作为本科生、研究生和博士生学习机器学习相关课程的教材，也可供高校计算机科学、人工智能、自动化等专业技术人员，以及对机器学习、人工智能感兴趣的研究人员和工程师参考。
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