摘要
结合高校大数据、人工智能类专业课程教学面临的困难,分析当前人才培养方案课程设置中存在的不足,提出本科大数据、人工智能类专业需开设最优化方法课程的建议。通过某矿场车辆分类和重庆市粮食产量预测及其影响因素分析等案例中涉及到的卷积神经网络及其迁移学习、支持向量机回归等机器学习方法,探讨了在大数据、人工智能专业开设最优化方法课程的必要性。
Combining the difficulties faced by the teaching of big data and artificial intelligence majors in universities,and analyzing the shortcomings in the curriculum of current talent training programs,the proposal that undergraduate big data and artificial intelligence majors need to offer optimization methods courses is put forward.Through the convolution neural network and its transfer learning,support vector machine regression and other machine learning methods involved in the cases of vehicle classification in a mine and the prediction and influencing factors of grain output in Chongqing,the necessity of offering optimization methods courses in big data and artificial intelligence majors is discussed.
作者
彭扬
李庆玉
任泽民
刘小翠
邹黎敏
Peng Yang;Li Qingyu;Ren Zemin;Liu Xiaocui;Zou Limin(School of Mathematical Physics and Data Science,Chongqing University of Science and Technology,Chongqing 401331,China;School of Mathematics and Statistics,Chongqing Technology and Business University)
出处
《计算机时代》
2022年第11期141-143,148,共4页
Computer Era
基金
重庆市高等教育教学改革研究重点项目(212123)
重庆科技学院本科教育教学改革研究重点项目(202108)。
关键词
大数据
人工智能
机器学习
数据挖掘
最优化方法
big data
artificial intelligence
machine learning
data mining
optimization method
