东北大学：某学院应用统计学专业《量化管理非线性方法》课程教学大纲

量化管理非线性方法教学大纲inNonlinearMethodsSubject SyllabusQuantitative Management一、课程信息SubjectInformation课程编号：开课学期：53100313009Subject IDSemester课程分类：所属课群：专业教育PA专业基础MFSectionCategory课程学分：总学时/周：348Credit PointsTotal Hours/Weeks理论学时：实验学时：480LECT.HoursEXP. Hours实践学时/周：PBL学时：00PBL HoursPRAC. Hours/Weeks开课学院：东北大学适用专业：应用统计学ASCollegeStream悉尼智能科技学院课程属性：课程模式：必修Compulsory互认EQVModePattern中方课程协调人于艳辉成绩记载方式百分制MarksNEU CoordinatorYanhui YuResult Type先修课程：高等数学建模RequisitesAdvanced Mathematical Modeling英文参考教材NONLINEARPROGRAMMING（THIRDEDITION)ENTextbooks中文参考教材《最优化方法》作者：张薇、薛嘉庆编著，出版社：东北大学出版社CN Textbooks教学资源https://lms.cloudcampus.com.cn/courses/37Resources于艳辉课程负责人(撰写人)：提交日期：单击或点击此处输Yanhui YuSubmitted Date入日期。Subject Director任课教师(含负责人)于艳辉Taught byYanhui Yu审核人：批准人：韩鹏史闻博Checked byApproved by批准日期：单击或点击此处输Approved Date入日期。1/9

1 / 9 量化管理非线性方法 教学大纲 Subject Syllabus Nonlinear Methods in Quantitative Management 一、课程信息 Subject Information 课程编号: Subject ID 3100313009 开课学期: Semester 5 课程分类: Category 专业教育 PA 所属课群: Section 专业基础 MF 课程学分: Credit Points 3 总学时/周: Total Hours/Weeks 48 理论学时: LECT. Hours 48 实验学时: EXP. Hours 0 PBL 学时: PBL Hours 0 实践学时/周: PRAC. Hours/Weeks 0 开课学院: College 东北大学 悉尼智能科技学院 适用专业: Stream 应用统计学 AS 课程属性: Pattern 必修 Compulsory 课程模式: Mode 互认 EQV 中方课程协调人: NEU Coordinator 于艳辉 Yanhui Yu 成绩记载方式: Result Type 百分制 Marks 先修课程: Requisites 高等数学建模 Advanced Mathematical Modeling 英文参考教材: EN Textbooks NONLINEAR PROGRAMMING（THIRD EDITION） 中文参考教材: CN Textbooks 《最优化方法》作者:张薇、薛嘉庆编著,出版社:东北大学出版社 教学资源: Resources https://lms.cloudcampus.com.cn/courses/37 课程负责人(撰写人): Subject Director 于艳辉 Yanhui Yu 提交日期: Submitted Date 单击或点击此处输 入日期。 任课教师(含负责人): Taught by 于艳辉 Yanhui Yu 审核人: Checked by 韩鹏 批准人: Approved by 史闻博 批准日期: Approved Date 单击或点击此处输 入日期

二、教学目标SubjectLearningObjectives(SLOs)注：毕业要求及指标点可参照悉尼学院本科生培养方案，可根据实际情况增减行数Note: GA and index can be referred from undergraduate program in SSTC website. Please add/reduce lines based on subject《量化管理非线性方法》是应用统计学专业重要的专业基础课程之。通过本课程的学习，了解最优化方法的发展过程及发展方向，掌握最优化理论基础、最优性条件、线性搜索技术、最速下降法、牛顿法、共轭梯度法、拟牛顿法、罚函数法等内容，使学生掌握这些最优化方法的基本要点及理论性质，培养和提高学生解决相关实际问题的能力，为今后的实际工作奠定必要的基础。Nonlinear Methods in Quantitative Management is one of theimportant professional basic courses for applied statistics majors.整体目标：Through the study of this course, understand the development processOverall Objectiveand development direction of optimization methods.master thetheoreticalbasisof optimization,optimalityconditions,linearsearchtechnology, steepest descent method, Newton method, conjugategradient method, quasi-Newton method, penalty function It enablesstudents to master the basic points and theoretical properties of theseoptimization methods, cultivate and improve students'ability to solvepractical problems, and lay a necessary foundation for future practicalwork了解最优化方法的发展过程及发展方向1-1Understandthedevelopmentprocessanddevelopmentdirectionoftheoptimizationmethod掌握最优化理论基础、最优性条件1-2Master the theoretical basis of optimization and optimalityconditions掌握线性搜索技术、最速下降法1-3Master linear search techniques, steepest descent method（1）专业目标：掌握牛顿法、共轭梯度法Professional Ability1-4Master Newton's method, conjugate gradient method掌握DFP算法、步长加速法、最小二乘问题的解法1-5Master DFP algorithm, step size acceleration method, leastsquaresproblem solution掌握Zoutendijk容许方向法1-6Master theZoutendijk Admissible Direction Method掌握罚函数法1-7Master the penalty function method培养遵守法律、懂规则、守规则的新时代公民2-1Cultivate citizens of the new era who abide by the law,understand and obey therules（2）德育目标：了解主要矛盾和次要矛盾，在面对复杂问题的时候要实事求Essential Quality是、抓住主要矛盾2-2Understandthemaincontradictionandsecondarycontradiction, seek truth from facts and grasp the maincontradictioninthefaceofcomplexproblems2/9

2 / 9 二、教学目标 Subject Learning Objectives (SLOs) 注：毕业要求及指标点可参照悉尼学院本科生培养方案，可根据实际情况增减行数 Note: GA and index can be referred from undergraduate program in SSTC website. Please add/reduce lines based on subject. 整体目标: Overall Objective 《量化管理非线性方法》是应用统计学专业重要的专业基础课程之 一。通过本课程的学习，了解最优化方法的发展过程及发展方向， 掌握最优化理论基础、最优性条件、线性搜索技术、最速下降法、 牛顿法、共轭梯度法、拟牛顿法、罚函数法等内容，使学生掌握这 些最优化方法的基本要点及理论性质，培养和提高学生解决相关实 际问题的能力，为今后的实际工作奠定必要的基础。 Nonlinear Methods in Quantitative Management is one of the important professional basic courses for applied statistics majors. Through the study of this course, understand the development process and development direction of optimization methods, master the theoretical basis of optimization, optimality conditions, linear search technology, steepest descent method, Newton method, conjugate gradient method, quasi-Newton method, penalty function It enables students to master the basic points and theoretical properties of these optimization methods, cultivate and improve students' ability to solve practical problems, and lay a necessary foundation for future practical work. （1）专业目标: Professional Ability 1-1 了解最优化方法的发展过程及发展方向 Understand the development process and development direction of the optimization method 1-2 掌握最优化理论基础、最优性条件 Master the theoretical basis of optimization and optimality conditions 1-3 掌握线性搜索技术、最速下降法 Master linear search techniques, steepest descent method 1-4 掌握牛顿法、共轭梯度法 Master Newton's method, conjugate gradient method 1-5 掌握 DFP 算法、步长加速法、最小二乘问题的解法 Master DFP algorithm, step size acceleration method, least squares problem solution 1-6 掌握 Zoutendijk 容许方向法 Master the Zoutendijk Admissible Direction Method 1-7 掌握罚函数法 Master the penalty function method （2）德育目标: Essential Quality 2-1 培养遵守法律、懂规则、守规则的新时代公民 Cultivate citizens of the new era who abide by the law, understand and obey the rules 2-2 了解主要矛盾和次要矛盾，在面对复杂问题的时候要实事求 是、抓住主要矛盾 Understand the main contradiction and secondary contradiction, seek truth from facts and grasp the main contradiction in the face of complex problems

培养服务意识，具有“以人为本”的服务精神2-3Cultivate service consciousness and have the service spirit of"people-oriented"培养具有不畏困难、不惧失败、锲而不舍、敢于尝试、迎难而上的精神，并在学习过程中培养自己的细心和耐心的勇气和精神2-4Cultivate the spirit of not fearing difficulties or failureperseverance,daring to try,and cultivate their own careful andpatient courage and spirit in the process of learning培养有条理和计划，做到心中有数、有条不紊、循序渐进地完成一项工作2-5Cultivateasenseof order and plan,andcompletea work in anorderly andgradual manner课程教学目标与毕业要求的对应关系MatrixofGA&SLOs毕业要求GA指标点GAIndex教学目标SLOs1-1：具有较强的演绎推理能力、准确计算能力、分析归纳能力、抽象思维能力，掌握数学、自然科学和相关专业知识，并使用其建立正确的数学、物理学等模型以解释复杂实际问题1-1:Capable of deductive reasoning,1、理学知识：具有扎实的数calculation,analysisandaccurate学基础，能够将数学、自然inductionandabstractthinking科学和专业知识用于解决复Establishingcorrectmathematicaland杂实际问题physical models with the professional1. Science Knowledge: Applyknowledgeofmathematics,natural1-1 到1-7knowledgeof mathematics,science, etc. to solve complex practicalnatural science, fundamentalsproblemsandanengineering1-3：了解本专业涉及相关行业的发展趋specialization to the solution势以及相关产业的运营模式，具备在本ofcomplexengineering专业相关领域进行方案设计、技术创新problems的能力1-3:Understanding the development andoperations of related industries in thismajor,capable of conducting programdesign and technological innovation inrelated fields of this major4、研究：能够基于科学原理4-1：能够基于科学原理并采用科学方法并采用科学方法对复杂实际在本专业相关理论指导下对复杂实际问问题进行研究，包括设计实题设计实验进行研究1-1 到 1-7验、分析与解释数据、并通4-1:Capable of design experiments on过信息综合得到合理有效的complexproblemswithscientific结论knowledge and research methods of this4.ConductInvestigation:major3/9

3 / 9 2-3 培养服务意识，具有 “以人为本” 的服务精神 Cultivate service consciousness and have the service spirit of "people-oriented" 2-4 培养具有不畏困难、不惧失败、锲而不舍、敢于尝试、迎难 而上的精神，并在学习过程中培养自己的细心和耐心的勇气 和精神 Cultivate the spirit of not fearing difficulties or failure, perseverance, daring to try, and cultivate their own careful and patient courage and spirit in the process of learning 2-5 培养有条理和计划，做到心中有数、有条不紊、循序渐进地 完成一项工作 Cultivate a sense of order and plan, and complete a work in an orderly and gradual manner 课程教学目标与毕业要求的对应关系 Matrix of GA & SLOs 毕业要求 GA 指标点 GA Index 教学目标 SLOs 1、理学知识：具有扎实的数 学基础，能够将数学、自然 科学和专业知识用于解决复 杂实际问题 1. Science Knowledge: Apply knowledge of mathematics, natural science, fundamentals and an engineering specialization to the solution of complex engineering problems 1-1：具有较强的演绎推理能力、准确计 算能力、分析归纳能力、抽象思维能力， 掌握数学、自然科学和相关专业知识， 并使用其建立正确的数学、物理学等模 型以解释复杂实际问题 1-1: Capable of deductive reasoning, accurate calculation, analysis and induction and abstract thinking. Establishing correct mathematical and physical models with the professional knowledge of mathematics, natural science, etc. to solve complex practical problems 1-1 到 1-7 1-3：了解本专业涉及相关行业的发展趋 势以及相关产业的运营模式，具备在本 专业相关领域进行方案设计、技术创新 的能力 1-3: Understanding the development and operations of related industries in this major; capable of conducting program design and technological innovation in related fields of this major 4、研究：能够基于科学原理 并采用科学方法对复杂实际 问题进行研究，包括设计实 验、分析与解释数据、并通 过信息综合得到合理有效的 结论 4. Investigation: Conduct 4-1：能够基于科学原理并采用科学方法， 在本专业相关理论指导下对复杂实际问 题设计实验进行研究 4-1: Capable of design experiments on complex problems with scientific knowledge and research methods of this major 1-1 到 1-7

4-2：能够结合本专业知识对实验数据进investigationsofcomplexproblems行分析与解释，设计并优化实验方案，usingresearch-basedknowledge并通过信息综合得到合理有效的结论andresearchmethods4-2:Capable of analyzing and interpretingincludingdesignofthe experimental data, designing andanalysisandoptimizingthe experimental schemerwithexperiments,interpretationofdata,andtheknowledgeofthismajor,reasonablesynthesis of information toand effective conclusions are obtainedthrough information synthesisprovide valid conclusions4-3：能够追踪国际前沿技术动态，掌握本专业涉及的重要技术指标以及达到指标所需的技术途径4-3:Capable of trackingthe internationalcutting-edge technology trends; masteringthe important technical indicators involvedinthemajor and the technical approachesrequired to achieve the indicators三、教学内容Content(Topics)注：以中英文填写，各部分内容的表格可根据实际知识单元数量进行复制、扩展或缩减Note: Filled in both CN and EN,extend or reduce based on the actual numbers of knowledge unit(1）理论教学Lecture知识单元序号支撑教学目标11-1、1-2、2-2、2-4KnowledgeUnitNoSLOs Supported知识单元名称最优化理论基础Unit TitleOptimization theoretical basis最优化问题实例Examples ofoptimization problems最优化问题的基本概念Basicconceptsofoptimizationproblems二维问题的图解法Graphical method for two-dimensional problems知识点：梯度和Hesse矩阵Knowledge DeliveryGradients and Hesse matrices多元函数的Talor展开Talor expansion of multivariate functions凸函数与凸规划Convex functions and convex programming极小点的判定条件Judgment conditionsfor minimum point4/9

4 / 9 investigations of complex problems using research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of information to provide valid conclusions 4-2：能够结合本专业知识对实验数据进 行分析与解释，设计并优化实验方案， 并通过信息综合得到合理有效的结论 4-2: Capable of analyzing and interpreting the experimental data, designing and optimizing the experimental schemer with the knowledge of this major; reasonable and effective conclusions are obtained through information synthesis 4-3：能够追踪国际前沿技术动态，掌握 本专业涉及的重要技术指标以及达到指 标所需的技术途径 4-3: Capable of tracking the international cutting-edge technology trends; mastering the important technical indicators involved in the major and the technical approaches required to achieve the indicators 三、教学内容 Content (Topics) 注：以中英文填写，各部分内容的表格可根据实际知识单元数量进行复制、扩展或缩减 Note: Filled in both CN and EN, extend or reduce based on the actual numbers of knowledge unit (1) 理论教学 Lecture 知识单元序号: Knowledge Unit No. 1 支撑教学目标: SLOs Supported 1-1、1-2、2-2、2-4 知识单元名称 Unit Title 最优化理论基础 Optimization theoretical basis 知识点: Knowledge Delivery 最优化问题实例 Examples of optimization problems 最优化问题的基本概念 Basic concepts of optimization problems 二维问题的图解法 Graphical method for two-dimensional problems 梯度和 Hesse 矩阵 Gradients and Hesse matrices 多元函数的 Talor 展开 Talor expansion of multivariate functions 凸函数与凸规划 Convex functions and convex programming 极小点的判定条件 Judgment conditions for minimum point

了解：最优化发展的基本历史RecognizeBasichistoryofoptimaldevelopment理解：最优化问题实例UnderstandExamplesof an optimization problem最优化基本概念，二维问题的图解法，梯度和Hesse学习目标：矩阵，多元函数的Talor展开，凸函数与凸规划，极Learning Objectives小点的判定条件掌握：Basicconceptsofoptimization,graphicalmethodforMastertwo-dimensional problems, gradient and Hessematrices,Talorexpansionofmultivariatefunctionsconvex functions and convex programming, minimumpoint judgmentconditions了解主要矛盾和次要矛盾，在面对复杂问题的时候要实事求是、抓住主要矛盾Understand the main contradiction and secondary contradiction, seektruth from facts and grasp the main contradiction in the face of德育目标complexproblemsMoral Objectives培养具有不畏困难、不惧失败、锲而不舍、敢于尝试、迎难而上的精神，并在学习过程中培养自己的细心和耐心的勇气和精神Cultivate the spirit of not fearing difficulties or failure, perseverance,daring to try, and cultivate their own careful and patient courage andspirit intheprocess of learning梯度和Hesse矩阵，凸函数与凸规划，极小点的判定条件重点：Gradientsand Hessematrices,convexfunctionsand convexKey Pointsprogramming,minimumpointdeterminationconditions难点：多元函数的Talor展开Focal PointsTalor expansion ofmultivariate functions知识单元序号支撑教学目标1-3, 1-4, 1-5,2Knowledge Unit No.SLOs Supported2-4、2-5知识单元名称无约束优化问题Unit TitleUnconstrainedoptimizationproblem线性搜索技术Linear search technique最速下降法Steepest descentNewton法知识点：Newton's methodKnowledge DeliveryF-R共轭梯度法F-R conjugate gradient methodDFP算法DFP algorithm步长加速法5/9

5 / 9 学习目标: Learning Objectives 了解: Recognize 最优化发展的基本历史 Basic history of optimal development 理解: Understand 最优化问题实例 Examples of an optimization problem 掌握: Master 最优化基本概念，二维问题的图解法，梯度和 Hesse 矩阵，多元函数的 Talor 展开，凸函数与凸规划，极 小点的判定条件 Basic concepts of optimization, graphical method for two-dimensional problems, gradient and Hesse matrices, Talor expansion of multivariate functions, convex functions and convex programming, minimum point judgment conditions 德育目标 Moral Objectives 了解主要矛盾和次要矛盾，在面对复杂问题的时候要实事求是、抓 住主要矛盾 Understand the main contradiction and secondary contradiction, seek truth from facts and grasp the main contradiction in the face of complex problems 培养具有不畏困难、不惧失败、锲而不舍、敢于尝试、迎难而上的 精神，并在学习过程中培养自己的细心和耐心的勇气和精神 Cultivate the spirit of not fearing difficulties or failure, perseverance, daring to try, and cultivate their own careful and patient courage and spirit in the process of learning 重点: Key Points 梯度和 Hesse 矩阵，凸函数与凸规划，极小点的判定条件 Gradients and Hesse matrices, convex functions and convex programming, minimum point determination conditions 难点: Focal Points 多元函数的 Talor 展开 Talor expansion of multivariate functions 知识单元序号: Knowledge Unit No. 2 支撑教学目标: SLOs Supported 1-3，1-4，1-5， 2-4、2-5 知识单元名称 Unit Title 无约束优化问题 Unconstrained optimization problem 知识点: Knowledge Delivery 线性搜索技术 Linear search technique 最速下降法 Steepest descent Newton 法 Newton's method F-R 共轭梯度法 F-R conjugate gradient method DFP 算法 DFP algorithm 步长加速法

Stepacceleration最小二乘问题的解法Solution to the least squares problem线搜索算法的收敛性，熟悉各算法的理论性质理解：Convergenceof line searchalgorithms,familiarwithUnderstand学习目标：thetheoretical properties of each algorithmLearning Objectives本章各算法的计算步骤掌握：The calculation steps of each algorithm in thisMasterchapter培养具有不畏困难、不惧失败、锲而不舍、敢于尝试、迎难而上的精神，并在学习过程中培养自己的细心和耐心的勇气和精神Cultivate the spirit of not fearing difficulties or failure,perseverance,daring to try, and cultivate their own careful and patient courage and德育目标spirit in the process of learningMoral Objectives培养有条理和计划，做到心中有数、有条不紊、循序渐进地完成一项工作Cultivate a sense of order and plan,and complete a work in an orderlyand gradualmanner最速下降法的算法步骤The algorithm steps of the steepest descent methodNewton法的算法步骤Algorithmic steps of Newton's method重点：F-R共轭梯度法的算法步骤Key PointsAlgorithm steps of F-R conjugate gradient methodDFP算法的算法步骤Algorithmic steps of the DFP algorithm步长加速法的算法步骤AlgorithmstepsofstepaccelerationmethodF-R共轭梯度法的算法步骤难点：Algorithm steps of F-R conjugate gradient methodDFP算法的算法步骤FocalpointsAlgorithmic steps oftheDFP algorithm知识单元序号：支撑教学目标：31-6,1-7、2-5SLOs SupportedKnowledge Unit No知识单元名称约束优化问题Unit TitleConstrainedOptimizationProblem最优性条件Optimality condition知识点：Zoutendijk容许方向法Knowledge DeliveryZoutendijk admissible direction method罚函数法Penalty function method6/9

6 / 9 Step acceleration 最小二乘问题的解法 Solution to the least squares problem 学习目标: Learning Objectives 理解: Understand 线搜索算法的收敛性，熟悉各算法的理论性质 Convergence of line search algorithms, familiar with the theoretical properties of each algorithm 掌握: Master 本章各算法的计算步骤 The calculation steps of each algorithm in this chapter 德育目标 Moral Objectives 培养具有不畏困难、不惧失败、锲而不舍、敢于尝试、迎难而上的 精神，并在学习过程中培养自己的细心和耐心的勇气和精神 Cultivate the spirit of not fearing difficulties or failure, perseverance, daring to try, and cultivate their own careful and patient courage and spirit in the process of learning 培养有条理和计划，做到心中有数、有条不紊、循序渐进地完成一 项工作 Cultivate a sense of order and plan, and complete a work in an orderly and gradual manner 重点: Key Points 最速下降法的算法步骤 The algorithm steps of the steepest descent method Newton 法的算法步骤 Algorithmic steps of Newton's method F-R 共轭梯度法的算法步骤 Algorithm steps of F-R conjugate gradient method DFP 算法的算法步骤 Algorithmic steps of the DFP algorithm 步长加速法的算法步骤 Algorithm steps of step acceleration method 难点: Focal points F-R 共轭梯度法的算法步骤 Algorithm steps of F-R conjugate gradient method DFP 算法的算法步骤 Algorithmic steps of the DFP algorithm 知识单元序号: Knowledge Unit No. 3 支撑教学目标: SLOs Supported 1-6，1-7、2-5 知识单元名称 Unit Title 约束优化问题 Constrained Optimization Problem 知识点: Knowledge Delivery 最优性条件 Optimality condition Zoutendijk 容许方向法 Zoutendijk admissible direction method 罚函数法 Penalty function method

内点法简介Introduction to interior point method梯度投影法Gradient projection method熟悉各算法的理论性质理解：Familiar with the theoretical properties of eachUnderstandalgorithm学习目标：约束优化问题的最优性条件，本章各算法的理论性质Learning Objectives掌握：Optimality conditions for constrained optimizationMasterproblems, theoretical properties of the algorithms inthis chapter培养有条理和计划，做到心中有数、有条不紊、循序渐进地完成1项工作德育目标Moral ObjectivesCultivate a sense of order and plan, and complete a work in an orderlyand gradual manner最优性条件Optimality conditionZoutendijk容许方向法重点:Zoutendijk admissible direction methodKey Points罚函数法Penalty function method梯度投影法Gradient projection method难点：罚函数法Focal pointsPenaltyfunction method三、教学安排TeachingSchedule注：可根据实际情况增减行数Note: Please add/reduce lines based on subject.学时(周)Hour(Week)教学内容TeachingContent理论集中实践实验课外实践PBLLECT.EXP.PRAC.最优化理论基础12Optimization theoretical basis无约束优化问题24Unconstrained optimization problem约束优化问题12Constrainedoptimizationproblem48总计Total7/9

7 / 9 内点法简介 Introduction to interior point method 梯度投影法 Gradient projection method 学习目标: Learning Objectives 理解: Understand 熟悉各算法的理论性质 Familiar with the theoretical properties of each algorithm 掌握: Master 约束优化问题的最优性条件，本章各算法的理论 性质 Optimality conditions for constrained optimization problems, theoretical properties of the algorithms in this chapter 德育目标 Moral Objectives 培养有条理和计划，做到心中有数、有条不紊、循序渐进地完成一 项工作 Cultivate a sense of order and plan, and complete a work in an orderly and gradual manner 重点: Key Points 最优性条件 Optimality condition Zoutendijk 容许方向法 Zoutendijk admissible direction method 罚函数法 Penalty function method 梯度投影法 Gradient projection method 难点: Focal points 罚函数法 Penalty function method 三、教学安排 Teaching Schedule 注：可根据实际情况增减行数 Note: Please add/reduce lines based on subject. 教学内容 Teaching Content 学时(周) Hour(Week) 理论 LECT. 实验 EXP. 课外实践 PBL 集中实践 PRAC. 最优化理论基础 Optimization theoretical basis 12 无约束优化问题 Unconstrained optimization problem 24 约束优化问题 Constrained optimization problem 12 总计 Total 48

五、教学方法TeachingMethodology注：可根据实际情况增减行数或修改内容Note: Please add/reduce lines or revise content based on subject勾选Check教学方法与特色TeachingMethodology&Characters多媒体教学：基于信息化设备的课堂教学团Multi-media-based lecturing实践能力传授：理论与行业、实际案例相结合团Combining theory with industrial practical problems课程思政建设：知识讲授与德育相结合日Knowledgedeliverywith ethic educationPBL教学：问题驱动的分组学习与交流口Problem-based learning其他：单击或点击此处输入文字。口Other:单击或点击此处输入文字。六、成绩评定Assessment注：可根据实际情况增减行数或修改内容Note: Please add/reduce lines or revise content based on subject考核环节于艳辉环节负责人：平时BehaviorAssessment ContentDirectorYanhui Yu给分形式：课程总成绩比重(%):50百分制MarksResult TypePercentage (%)满分100分，出勤，20分：课堂小测验，30分：作业，50分。考核方式The full score is 100 points, attendance, 20 points; group work, 30Measurespoints; homework, 50 points.考核环节：环节负责人：于艳辉期末 FinalAssessment ContentDirectorYanhui Yu给分形式课程总成绩比重(%)：50百分制MarksResult TypePercentage (%)满分100分，通过批阅期末考试试卷给出学生成绩。考核方式The full score is 10o, and the students' scores are given by marking theMeasuresfinal examination papers七、改进机制ImprovementMechanism注：未尽事宜以教学团队以及学院教学指导委员会商定为准Note: Matters not covered in this file shall be determined by TAB of SSTC, NEU.教学大纲改进机制SubjectSyllabusImprovementMechanism考核周期(年)修订周期(年)：4.4Check Period (YR)Revise Period (YR)8/9

8 / 9 五、教学方法 Teaching Methodology 注：可根据实际情况增减行数或修改内容 Note: Please add/reduce lines or revise content based on subject. 勾选 Check 教学方法与特色 Teaching Methodology & Characters  多媒体教学：基于信息化设备的课堂教学 Multi-media-based lecturing  实践能力传授：理论与行业、实际案例相结合 Combining theory with industrial practical problems  课程思政建设：知识讲授与德育相结合 Knowledge delivery with ethic education ☐ PBL 教学：问题驱动的分组学习与交流 Problem-based learning ☐ 其他:单击或点击此处输入文字。 Other:单击或点击此处输入文字。 六、成绩评定 Assessment 注：可根据实际情况增减行数或修改内容 Note: Please add/reduce lines or revise content based on subject. 考核环节: Assessment Content 平时 Behavior 环节负责人: Director 于艳辉 Yanhui Yu 给分形式: Result Type 百分制 Marks 课程总成绩比重(%): Percentage (%) 50 考核方式: Measures 满分 100 分，出勤，20 分；课堂小测验，30 分；作业，50 分。 The full score is 100 points, attendance, 20 points; group work, 30 points; homework, 50 points. 考核环节: Assessment Content 期末 Final 环节负责人: Director 于艳辉 Yanhui Yu 给分形式: Result Type 百分制 Marks 课程总成绩比重(%): Percentage (%) 50 考核方式: Measures 满分 100 分，通过批阅期末考试试卷给出学生成绩。 The full score is 100, and the students' scores are given by marking the final examination papers. 七、改进机制 Improvement Mechanism 注：未尽事宜以教学团队以及学院教学指导委员会商定为准。 Note: Matters not covered in this file shall be determined by TAB of SSTC, NEU. 教学大纲改进机制 Subject Syllabus Improvement Mechanism 考核周期(年): Check Period (YR) 4 修订周期(年): Revise Period (YR) 4

课程负责人根据课程教学内容与人才培养目标组织课程团队讨论并修改教学大纲，报分管教学工作副院长审核后由执行院长批准。改进措施：The subject coordinator shall be responsible for the syllabus discussionMeasuresand improvement, and the revised version shall be submitted to deputydean (teaching affairs)for reviewing then to executive dean forapproval成绩评定改进机制AssessmentImprovementMechanism考核周期(年):修订周期(年):11Check Period (YR)RevisePeriod (YR)课程负责人根据课程教学内容、课堂教学效果以及成绩分布，对课程教学方法和成绩评定环节进行改进，并同步优化评定办法。改进措施The subject coordinator shall revise the syllabus based on the teachingMeasurescontent, effect and result distribution while optimize the assessmentmeasures.9/9

9 / 9 改进措施: Measures 课程负责人根据课程教学内容与人才培养目标组织课程团队讨论 并修改教学大纲，报分管教学工作副院长审核后由执行院长批准。 The subject coordinator shall be responsible for the syllabus discussion and improvement, and the revised version shall be submitted to deputy dean (teaching affairs) for reviewing then to executive dean for approval 成绩评定改进机制 Assessment Improvement Mechanism 考核周期(年): Check Period (YR) 1 修订周期(年): Revise Period (YR) 1 改进措施: Measures 课程负责人根据课程教学内容、课堂教学效果以及成绩分布，对课 程教学方法和成绩评定环节进行改进，并同步优化评定办法。 The subject coordinator shall revise the syllabus based on the teaching content, effect and result distribution while optimize the assessment measures