东北大学：某学院应用统计学专业《机器学习》课程教学大纲

机器学习教学大纲MachineLearning Subject Syllabus一，课程信息SubjectInformation课程编号：开课学期：53100313015Subject IDSemester课程分类：所属课群：专业教育PA专业方向类CategorySection课程学分：总学时/周：2.540 Credit PointsTotal Hours/Weeks理论学时：实验学时：400LECT. HoursEXP. HoursPBL学时:实践学时/周：00PBL HoursPRAC.Hours/Weeks开课学院：东北大学适用专业：应用统计学 ASCollege悉尼智能科技学院Stream课程属性：课程模式：选修Elective自建NEUModePattern张琨中方课程协调人成绩记载方式：百分制MarksNEU CoordinatorResult TypeKun Zhang先修课程：数理统计RequisitesMathematical Statistics英文参考教材：Peter Harrington.Machine Learning in Action.Manning Publications,ENTextbooks2012中文参考教材：周志华，《机器学习》，清华大学出版社，2016CN Textbooks教学资源：Resources张琨课程负责人(撰写人)：提交日期：单击或点击此处输Subject DirectorSubmitted Date入日期。Kun Zhang张琨任课教师(含负责人):Taught byKun Zhang审核人：批准人：韩鹏史闻博Checked byApprovedby批准日期：单击或点击此处输Approved Date入日期。1/9

1 / 9 机器学习 教学大纲 Machine Learning Subject Syllabus 一、课程信息 Subject Information 课程编号: Subject ID 3100313015 开课学期: Semester 5 课程分类: Category 专业教育 PA 所属课群: Section 专业方向类 课程学分: Credit Points 2.5 总学时/周: Total Hours/Weeks 40 理论学时: LECT. Hours 40 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 选修 Elective 课程模式: Mode 自建 NEU 中方课程协调人: NEU Coordinator 张琨 Kun Zhang 成绩记载方式: Result Type 百分制 Marks 先修课程: Requisites 数理统计 Mathematical Statistics 英文参考教材: EN Textbooks Peter Harrington. Machine Learning in Action. Manning Publications, 2012 中文参考教材: CN Textbooks 周志华，《机器学习》，清华大学出版社，2016 教学资源: Resources 课程负责人(撰写人): Subject Director 张琨 Kun Zhang 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 张琨 Kun Zhang 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通过本课程的学习，学生掌握适合在计算机上使用的概率、统计、代数、优化等方法以及与此相关的理论，掌握一些经典而且常用的机器学习方法，为专业课学习和参加工程实践打下必要的基础。Through the study of this course, students will master methods such as整体目标：probability, statistics, algebra, optimization, and related theoriesOverallObjectivesuitableforuseon computers.Theywillalsomastersome classicandcommonlyused machine learning methods,laying a necessaryfoundation for professional courses and participating in engineeringpractice.掌握假设空间、经验误差与过拟合、性能度量等机器学习的基础知识1-1 Master the basic knowledge of machine learning such ashypothesis space, empirical error and overfitting, performancemeasurement,etc掌握对数几率回归、决策树、神经网络、支持向量机等经典而常用的机器学习算法（1）专业目标：1-2Mastering classic and commonly used machine learningProfessional Abilityalgorithms such as logarithmic probability regression, decisiontrees, neural networks, and support vector machines能利用相关软件实现机器学习算法，学会用计算机求解科学技术问题1-3 Able touse relevant software to implement machine learningalgorithms and learnto use computers to solve scientificandtechnological problems培养科学与工程应用的意识和素质2-1Cultivate awareness and quality of scientific and engineeringapplications（2）德育目标：逐步培养学生的探索精神和创新能力2-2Essential QualityGradually cultivate students'exploratory spirit and innovativeability为将来从事相关研究奠定基础2-3Laying the foundation for future research in related fields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SLOs2/9

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 通过本课程的学习，学生掌握适合在计算机上使用的概率、统计、 代数、优化等方法以及与此相关的理论，掌握一些经典而且常用的 机器学习方法，为专业课学习和参加工程实践打下必要的基础。 Through the study of this course, students will master methods such as probability, statistics, algebra, optimization, and related theories suitable for use on computers. They will also master some classic and commonly used machine learning methods, laying a necessary foundation for professional courses and participating in engineering practice. （1）专业目标: Professional Ability 1-1 掌握假设空间、经验误差与过拟合、性能度量等机器学习的 基础知识 Master the basic knowledge of machine learning such as hypothesis space, empirical error and overfitting, performance measurement, etc 1-2 掌握对数几率回归、决策树、神经网络、支持向量机等经典 而常用的机器学习算法 Mastering classic and commonly used machine learning algorithms such as logarithmic probability regression, decision trees, neural networks, and support vector machines 1-3 能利用相关软件实现机器学习算法，学会用计算机求解科学 技术问题 Able to use relevant software to implement machine learning algorithms and learn to use computers to solve scientific and technological problems （2）德育目标: Essential Quality 2-1 培养科学与工程应用的意识和素质 Cultivate awareness and quality of scientific and engineering applications 2-2 逐步培养学生的探索精神和创新能力 Gradually cultivate students' exploratory spirit and innovative ability 2-3 为将来从事相关研究奠定基础 Laying the foundation for future research in related fields 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

3-1：能够设计针对本专业相关复杂实际问题的解决方案3-1: Capable of designing solutions to3、设计/开发解决方案：能complex practical problems related to this够设计针对复杂实际问题的major解决方案，设计满足特定需3-2：能够对不同设计方案进行比较和优求的系统、单元或流程，并化，在工作各环节中具有创新意识和批能够在设计环节中体现创新判意识，善于发现、分析、系统表述和意识，考虑社会、健康、安解决实际问题全、法律、文化以及环境等3-2: Capable of comparing and optimizing因素different design schemes, having a sense of3.Design/Developmentinnovation and criticism in all aspects of1-1 到 1-3ofSolutions: Design solutionswork,andbegoodatdiscovering,2-1到2-3forcomplexpracticalanalyzing,systematically elaborating andproblems and design systems,solvingpractical problemscomponents or processes that3-3：能够在设计和开发的各个环节中综meet specified needsswith合考虑社会、健康、安全、法律、文化appropriate consideration for以及环境等因素public health, and safety,3-2: Capable of comparing and optimizingcultural,societalanddifferent design schemes, having a sense ofenvironmental considerations.innovation and criticism in all aspects ofwork,and begood at discovering,analyzing,systematically elaborating andsolving practical problems4、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 thismajor4.Investigation:Conduct4-2：能够结合本专业知识对实验数据进1-1到1-3investigationsofcomplexusing行分析与解释，设计并优化实验方案，2-1 到 2-3problemsknowledgeresearch-based并通过信息综合得到合理有效的结论andresearchmethods4-2:Capableof analyzing and interpretingincludingdesignofthe experimental data, designing andanalysisandexperiments,optimizing the experimental schemer withinterpretationofdata,andthe knowledge of this major, reasonablesynthesis of information toand effective conclusions are obtainedprovidevalid conclusionsthrough information synthesis3/9

3 / 9 3、设计/开发解决方案：能 够设计针对复杂实际问题的 解决方案，设计满足特定需 求的系统、单元或流程，并 能够在设计环节中体现创新 意识，考虑社会、健康、安 全、法律、文化以及环境等 因素 3. Design/Development of Solutions: Design solutions for complex practical problems and design systems, components or processes that meet specified needs with appropriate consideration for public health, and safety, cultural, societal and environmental considerations. 3-1：能够设计针对本专业相关复杂实际 问题的解决方案 3-1: Capable of designing solutions to complex practical problems related to this major 1-1 到 1-3 2-1 到 2-3 3-2：能够对不同设计方案进行比较和优 化，在工作各环节中具有创新意识和批 判意识，善于发现、分析、系统表述和 解决实际问题 3-2: Capable of comparing and optimizing different design schemes, having a sense of innovation and criticism in all aspects of work, and be good at discovering, analyzing, systematically elaborating and solving practical problems 3-3：能够在设计和开发的各个环节中综 合考虑社会、健康、安全、法律、文化 以及环境等因素 3-2: Capable of comparing and optimizing different design schemes, having a sense of innovation and criticism in all aspects of work, and be good at discovering, analyzing, systematically elaborating and solving practical problems 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 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 4-1：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 1-3 2-1 到 2-3 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

5-2熟悉解决本专业相关复杂实际问题所需的技术和资源，能够运用现代信息5、使用现代工具：能够针对技术进行文献检索和资料查询，获取专复杂实际问题，开发、选择业解决方案与使用恰当的技术、资源、5-2: Familiar with the technology and现代信息技术工具，包括对resourcesrequiredtosolvecomplex复杂实际问题的预测与模practical problems related to the major,拟，并能够理解其局限性capable of using moderm information5. Modern Tool Usage:technology to conduct document retrieval1-1 到 1-3Create,selectandapplyand data query, and obtaining professional2-1到2-3appropriatetechniques,solutionsandmodernresources5-3：能够针对本专业相关复杂实际问题andITtools,engineering.选择与使用恰当的技术、资源、现代信includingpredictionand息技术工具modeling,tocomplex5-3:Capable of selecting and usingpractical problems, with anappropriate technology,resources, andoftheunderstandingmodern information technology tools inlimitationsresponse to complexpractical problemsrelated to the major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：1-1、2-1到 2-31Knowledge Unit NoSLOs Supported知识单元名称机器学习的基础知识FundamentalsofMachineLearningUnit Title经验误差与过拟合Empirical error and overftting评估方法Evaluationmethod知识点：性能度量Knowledge DeliveryPerformancemetrics比较检验Comparativetesting偏差与方差Deviation and variance了解:机器学习的产生与发展RecognizeTheemergence anddevelopment of machinelearning学习目标理解：比较检验Learning ObjectivesUnderstandComparative testing掌握：经验误差与过拟合、偏差与方差4/9

4 / 9 5、使用现代工具：能够针对 复杂实际问题，开发、选择 与使用恰当的技术、资源、 现代信息技术工具，包括对 复杂实际问题的预测与模 拟，并能够理解其局限性 5. Modern Tool Usage: Create, select and apply appropriate techniques, resources and modern engineering and IT tools, including prediction and modeling, to complex practical problems, with an understanding of the limitations 5-2 熟悉解决本专业相关复杂实际问题 所需的技术和资源，能够运用现代信息 技术进行文献检索和资料查询，获取专 业解决方案 5-2: Familiar with the technology and resources required to solve complex practical problems related to the major, capable of using modern information technology to conduct document retrieval and data query, and obtaining professional solutions 1-1 到 1-3 2-1 到 2-3 5-3：能够针对本专业相关复杂实际问题， 选择与使用恰当的技术、资源、现代信 息技术工具 5-3: Capable of selecting and using appropriate technology, resources, and modern information technology tools in response to complex practical problems related to the major 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、2-1 到 2-3 知识单元名称 Unit Title 机器学习的基础知识 Fundamentals of Machine Learning 知识点: Knowledge Delivery 经验误差与过拟合 Empirical error and overfitting 评估方法 Evaluation method 性能度量 Performance metrics 比较检验 Comparative testing 偏差与方差 Deviation and variance 学习目标: Learning Objectives 了解: Recognize 机器学习的产生与发展 The emergence and development of machine learning 理解: Understand 比较检验 Comparative testing 掌握: 经验误差与过拟合、偏差与方差

MasterEmpirical errorandoverfitting,deviationandvariance培养科学与工程应用的意识和素质Cultivate awareness and quality of scientific and engineeringapplications德育目标逐步培养学生的探索精神和创新能力Moral ObjectivesGradually cultivate students'exploratory spirit and innovative ability为将来从事相关研究奠定基础Laying the foundation for future research in related fields评估方法重点：Evaluation methodKey Points性能度量Performance metrics难点：偏差与方差Focal PointsDeviation and variance知识单元序号支撑教学目标：21-2、1-3、2-1到2-3Knowledge Unit No.SLOs Supported知识单元名称经典而常用的机器学习方法Unit TitleClassic and commonly used machine learning methods线性模型(Linear model决策树)Decision Tree神经网络Neural network知识点：支持向量机Knowledge DeliverySupport Vector Machine贝叶斯分类器Bayesian classifier集成学习Integrated learning聚类Clustering了解:各类算法的发展RecognizeDevelopmentofvarious algorithms理解：机器学习方法的理论推导学习目标：UnderstandTheoretical derivation of machine learning methodsLearning Objectives机器学习方法的应用及编程实现掌握：ApplicationandProgrammingImplementationofMasterMachine Learning Methods培养科学与工程应用的意识和素质Cultivate awareness and quality of scientific and engineering德育目标applicationsMoral Objectives逐步培养学生的探索精神和创新能力Gradually cultivate students' exploratory spirit and innovative ability5/9

5 / 9 Master Empirical error and overfitting, deviation and variance 德育目标 Moral Objectives 培养科学与工程应用的意识和素质 Cultivate awareness and quality of scientific and engineering applications 逐步培养学生的探索精神和创新能力 Gradually cultivate students' exploratory spirit and innovative ability 为将来从事相关研究奠定基础 Laying the foundation for future research in related fields 重点: Key Points 评估方法 Evaluation method 性能度量 Performance metrics 难点: Focal Points 偏差与方差 Deviation and variance 知识单元序号: Knowledge Unit No. 2 支撑教学目标: SLOs Supported 1-2、1-3、2-1 到 2-3 知识单元名称 Unit Title 经典而常用的机器学习方法 Classic and commonly used machine learning methods 知识点: Knowledge Delivery 线性模型 (Linear model 决策树) Decision Tree 神经网络 Neural network 支持向量机 Support Vector Machine 贝叶斯分类器 Bayesian classifier 集成学习 Integrated learning 聚类 Clustering 学习目标: Learning Objectives 了解: Recognize 各类算法的发展 Development of various algorithms 理解: Understand 机器学习方法的理论推导 Theoretical derivation of machine learning methods 掌握: Master 机器学习方法的应用及编程实现 Application and Programming Implementation of Machine Learning Methods 德育目标 Moral Objectives 培养科学与工程应用的意识和素质 Cultivate awareness and quality of scientific and engineering applications 逐步培养学生的探索精神和创新能力 Gradually cultivate students' exploratory spirit and innovative ability

为将来从事相关研究奠定基础Laying the foundation for future research in related fields神经网络Neural network重点：支持向量机Key PointsSupport Vector Machine贝叶斯分类器Bayesian classifier难点：集成学习Focal PointsIntegrated learning知识单元序号支撑教学目标31-2、1-3、2-1到2-3Knowledge Unit No.SLOs Supported知识单元名称进阶知识Unit TitleAdvanced knowledge卷积神经网络Convolutional neuralnetwork知识点：迁移学习Knowledge DeliveryTransfer learning元学习Meta learning了解：元学习RecognizeMeta learning学习目标：理解:迁移学习Learning ObjectivesUnderstandTransfer learning掌握：卷积神经网络MasterConvolutional neural network培养科学与工程应用的意识和素质Cultivate awareness and quality of scientific and engineeringapplications德育目标逐步培养学生的探索精神和创新能力Moral ObjectivesGradually cultivate students'exploratory spirit and innovative ability为将来从事相关研究奠定基础Laying the foundation for future research in related fields重点：卷积神经网络Key PointsConvolutional neural network难点：迁移学习Focal PointsTransfer learning(2）实验教学Experiments注：可根据实际情况增减行数。实验类型可分为验证性、设计性、综合性，实验性质可分为选做、必做。Note: Please add/reduce lines based on subject. The Type contains Verify, Design, and Comprehensive, while the Patterncontains Required and Elective无6/9

6 / 9 为将来从事相关研究奠定基础 Laying the foundation for future research in related fields 重点: Key Points 神经网络 Neural network 支持向量机 Support Vector Machine 贝叶斯分类器 Bayesian classifier 难点: Focal Points 集成学习 Integrated learning 知识单元序号: Knowledge Unit No. 3 支撑教学目标: SLOs Supported 1-2、1-3、2-1 到 2-3 知识单元名称 Unit Title 进阶知识 Advanced knowledge 知识点: Knowledge Delivery 卷积神经网络 Convolutional neural network 迁移学习 Transfer learning 元学习 Meta learning 学习目标: Learning Objectives 了解: Recognize 元学习 Meta learning 理解: Understand 迁移学习 Transfer learning 掌握: Master 卷积神经网络 Convolutional neural network 德育目标 Moral Objectives 培养科学与工程应用的意识和素质 Cultivate awareness and quality of scientific and engineering applications 逐步培养学生的探索精神和创新能力 Gradually cultivate students' exploratory spirit and innovative ability 为将来从事相关研究奠定基础 Laying the foundation for future research in related fields 重点: Key Points 卷积神经网络 Convolutional neural network 难点: Focal Points 迁移学习 Transfer learning (2) 实验教学 Experiments 注：可根据实际情况增减行数。实验类型可分为验证性、设计性、综合性，实验性质可分为选做、必做。 Note: Please add/reduce lines based on subject. The Type contains Verify, Design, and Comprehensive, while the Pattern contains Required and Elective 无

None四、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理论实验课外实践集中实践EXP.PBLLECT.PRAC.机器学习的基础知识4Fundamentalsof MachineLearning线性模型4(Linear model决策树)4Decision Tree神经网络4Neural network支持向量机4Support Vector Machine贝叶斯分类器4Bayesian classifier集成学习4Integrated learning聚类4Clustering卷积神经网络4Convolutional neural network迁移学习2Transfer learning元学习2Metalearning400总计Total0五、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团实践能力传授：理论与行业、实际案例相结合7/9

7 / 9 None 四、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. 机器学习的基础知识 Fundamentals of Machine Learning 4 线性模型 (Linear model 4 决策树) Decision Tree 4 神经网络 Neural network 4 支持向量机 Support Vector Machine 4 贝叶斯分类器 Bayesian classifier 4 集成学习 Integrated learning 4 聚类 Clustering 4 卷积神经网络 Convolutional neural network 4 迁移学习 Transfer learning 2 元学习 Meta learning 2 总计 Total 40 0 0 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

Combiningtheorywithindustrial practical problems课程思政建设：知识讲授与德育相结合团Knowledge delivery with ethic educationPBL教学：问题驱动的分组学习与交流团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张考核环节：环节负责人：平时BehaviorDirectorAssessmentContentKunZhang给分形式：课程总成绩比重(%)：30百分制MarksPercentage (%)Result Type考核方式：满分100分，出勤，50分：作业，50分。MeasuresFull scoreof 100points,attendance,50points;Homework,50points.张琨考核环节：环节负责人：期末FinalDirectorAssessmentContentKun Zhang给分形式：课程总成绩比重(%)：70百分制MarksResult TypePercentage (%)满分100分，通过批阅结课论文给出学生成绩。考核方式Full score of 100 points, providing student grades through reviewingMeasuresthe final thesis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)：44Check Period (YR)Revise Period (YR)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 responsibleforthe syllabus discussionMeasuresand improvement,and therevised version shall be submitted to deputydean (teaching affairs)for reviewing then to executive dean forapproval8/9

8 / 9 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 张琨 Kun Zhang 给分形式: Result Type 百分制 Marks 课程总成绩比重(%): Percentage (%) 30 考核方式: Measures 满分 100 分，出勤，50 分；作业，50 分。 Full score of 100 points, attendance, 50 points; Homework, 50 points. 考核环节: Assessment Content 期末 Final 环节负责人: Director 张琨 Kun Zhang 给分形式: Result Type 百分制 Marks 课程总成绩比重(%): Percentage (%) 70 考核方式: Measures 满分 100 分，通过批阅结课论文给出学生成绩。 Full score of 100 points, providing student grades through reviewing the final thesis 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 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AssessmentImprovementMechanism考核周期(年)修订周期（年）：11Check Period (YR)Revise Period (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 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