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(jī)器學(xué)習(xí)作為人工智能最重要的技術(shù)和工具，已成功應(yīng)用于解決各種復(fù)雜問(wèn)題。本書(shū)在簡(jiǎn)略介紹機(jī)器學(xué)習(xí)的基本方法與算法的基礎(chǔ)上，通過(guò)搜集典型復(fù)雜問(wèn)題的人工智能解決方案，諸如手寫數(shù)字識(shí)別、雷達(dá)自動(dòng)目標(biāo)識(shí)別、癌癥診斷、超強(qiáng)噪聲污染情況下的圖像過(guò)濾、基因芯片異質(zhì)性校正、孕婦子癇前期風(fēng)險(xiǎn)預(yù)測(cè)，以及一些典型的組合優(yōu)化問(wèn)題，如多約束最短路徑問(wèn)題和旅行商問(wèn)題等，考察如何運(yùn)用機(jī)器學(xué)習(xí)技術(shù)，創(chuàng)造解決復(fù)雜問(wèn)題的有效方法和算法，并通過(guò)這些案例窺視出人工智能技術(shù)的巨大優(yōu)勢(shì)和其面臨的極其嚴(yán)峻的挑戰(zhàn)。
本書(shū)可作為本科生、研究生和博士生學(xué)習(xí)機(jī)器學(xué)習(xí)相關(guān)課程的教材，也可供高校計(jì)算機(jī)科學(xué)、人工智能、自動(dòng)化等專業(yè)技術(shù)人員，以及對(duì)機(jī)器學(xué)習(xí)、人工智能感興趣的研究人員和工程師參考。