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.
機器學習作為人工智能最重要的技術和工具，已成功應用于解決各種復雜問題。本書在簡略介紹機器學習的基本方法與算法的基礎上，通過搜集典型復雜問題的人工智能解決方案，諸如手寫數字識別、雷達自動目標識別、癌癥診斷、超強噪聲污染情況下的圖像過濾、基因芯片異質性校正、孕婦子癇前期風險預測，以及一些典型的組合優化問題，如多約束最短路徑問題和旅行商問題等，考察如何運用機器學習技術，創造解決復雜問題的有效方法和算法，并通過這些案例窺視出人工智能技術的巨大優勢和其面臨的極其嚴峻的挑戰。
本書可作為本科生、研究生和博士生學習機器學習相關課程的教材，也可供高校計算機科學、人工智能、自動化等專業技術人員，以及對機器學習、人工智能感興趣的研究人員和工程師參考。
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