智能科学与技术专业人才培养方案
Undergraduate Program for Intelligent Science and Technology Major
学科门类: 工学 代码: 08
Discipline Type: Engineering Code: 08
类 别: 计算机类 代码: 0809
Type: Computer Code: 0809
专业名称: 智能科学与技术 代码: 080907T
Title of the Major: Intelligent Science and Technology Code: 080907T
一、学制与学位Length of Schooling and Degree
学制:四年 Duration:Four years
学位:工学学士 Degree:Bachelor of Engineering
二、培养目标 Educational Objectives
培养具备良好的人文、科学与工程素质,系统地掌握智能科学与技术、计算机、自动化的基本理论、基本知识和基本技能与方法,在智能科学与工程领域具有较强的科学研究能力和创新创业能力,具有良好的科学思维方法和系统的工程实践技术,具有良好的职业道德,能综合运用交叉知识与国际接轨的复合型、创造型科技人才。毕业生具有在工程技术、社会经济等各领域进行创新创业的能力,可以在民用或军用各部门、科研机构、高等院校、工厂企业等单位从事智能系统分析与设计、智能技术研发与应用等工作。
本专业预期学生在毕业五年左右能够达到的目标如下:
具有健全的人格和良好的人文素养与品德修养;
具有将专业知识用于问题分析、技术方案设计和运用工程技术解决问题的实际工作能力;
能在智能科学与技术相关领域胜任智能系统的运行与维护、智能装备的研发、生产、制造等工作;
能顺利实现就业或进入硕士、博士阶段进一步深造,有不断学习适应社会发展和行业竞争的能力;
具有在团队中分工协作、交流沟通的能力,能胜任技术负责、经营与管理等工作。
This major aims to cultivate excellent talents who are equipped with good humanities, science and engineering qualities, who systematically master the basic theories, basic knowledge and basic skills and methods of intelligent science and technology, computer and automation, and have strong scientific research capabilities and innovative entrepreneurship in the field of intelligent science and engineering. Graduates should have a good scientific thinking method and systematic engineering practice technology, with a good professional ethics, can comprehensively use cross-knowledge and international integration of composite, creative technology. They should also have the ability to innovate in various fields such as engineering technology, social economy, etc. They can engage in intelligent system analysis and design, intelligent technology research and development and application in civil or military departments, scientific research institutions, universities, factories and other institutions. jobs.
The goals that students are expected to achieve in the five years of graduation are as follows:
• Have a sound personality and good human qualities and moral cultivation;
• Have the practical ability to apply expertise to problem analysis, technical solution design, and engineering techniques to solve problems;
• Be able to operate and maintain intelligent systems, research and development, production and manufacturing of intelligent equipment in areas related to intelligent science and technology;
• Can successfully achieve employment or enter the master's and doctoral stages for further study, and have the ability to continuously learn to adapt to social development and industry competition;
• Be able to work and communicate in a team, capable of technical responsibility and management.
三、专业培养基本要求 Skills Profile
毕业生应获得以下几方面的知识和能力:
1.工程知识:能够将数学、自然科学、工程基础和专业知识用于解决智能系统相关领域的复杂工程问题。
2.问题分析:能够应用数学、自然科学和工程科学的基本原理,识别、表达、并通过文献研究分析智能系统及能源电力相关领域的复杂工程问题,以获得有效结论。
3.设计/开发解决方案:能够设计针对复杂工程问题的解决方案,设计满足特定需求的智能系统、单元(部件)或工艺流程,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。
4.研究:能够基于科学原理并采用科学方法对智能系统及能源电力相关领域的复杂工程问题进行研究,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。
5.使用现代工具:能够针对复杂工程问题,开发、选择与使用恰当的技术、资源、现代工程工具和信息技术工具,包括对智能系统相关领域的复杂工程问题的预测与模拟,并能够理解其局限性。
6.工程与社会:能够基于工程相关背景知识进行合理分析,评价专业工程实践和复杂工程问题解决方案对社会、健康、安全、法律以及文化的影响,并理解应承担的责任。
7.环境和可持续发展:能够理解和评价针对复杂工程问题的工程实践对环境、社会可持续发展的影响。
8.职业规范:具有人文社会科学素养、社会责任感,能够在工程实践中理解并遵守工程职业道德和规范,履行责任。
9.个人和团队:能够在多学科背景下的团队中承担个体、团队成员以及负责人的角色。
10.沟通:能够就智能系统及能源电力相关领域的复杂工程问题与业界同行及社会公众进行有效沟通和交流,包括撰写报告和设计文稿、陈述发言、清晰表达或回应指令。并具备一定的国际视野,能够在跨文化背景下进行沟通和交流。
11.项目管理:理解并掌握工程管理原理与经济决策方法,并能在多学科环境中应用。
12.学习:具有自主学习和终身学习的意识,有不断学习和适应发展的能力。
The required knowledge and ability for the graduates as follows:
1. Engineering knowledge: Ability to apply mathematics, natural sciences, engineering fundamentals and expertise to solve complex engineering problems in the field of intelligent systems.
2. Problem Analysis: It can apply the basic principles of mathematics, natural science and engineering science to identify, express, and analyze complex engineering problems in intelligent systems and energy and power related fields through literature research to obtain effective conclusions.
3. Design/Development Solutions: Ability to design solutions for complex engineering problems, design intelligent systems, units (components) or processes that meet specific needs, and reflect innovation in the design process, considering social, health and safety, legal, cultural and environmental factors.
4. Research: It is possible to conduct research on complex engineering problems in intelligent systems and energy and power related fields based on scientific principles and scientific methods, including designing experiments, analyzing and interpreting data, and obtaining reasonable and effective conclusions through information synthesis.
5. Use modern tools: Ability to develop, select and use appropriate technologies, resources, modern engineering tools and information technology tools for complex engineering problems, including predictions and simulations of complex engineering problems in areas related to intelligent systems, and understand them limitation.
6. Engineering and Society: Ability to conduct a rational analysis based on engineering-related background knowledge, evaluate the impact of professional engineering practices and complex engineering problem solutions on society, health, safety, law, and culture, and understand the responsibilities.
7. Environment and Sustainable Development: Ability to understand and evaluate the impact of engineering practices on complex engineering issues on environmental and social sustainability.
8. Professional norms: With humanities and social science literacy and social responsibility, we can understand and abide by engineering professional ethics and norms and fulfill our responsibilities in engineering practice.
9. Individuals and teams: Ability to assume the roles of individuals, team members, and responsible individuals in a multidisciplinary team.
10. Communication: Effective communication and communication with industry peers and the public on complex engineering issues related to intelligent systems and energy and power, including writing reports and designing contributions, presenting statements, articulating or responding to instructions. It also has a certain international perspective and can communicate and communicate in a cross-cultural context.
11. Project Management: Understand and master engineering management principles and economic decision-making methods, and apply them in a multidisciplinary environment.
12. Lifelong learning: Awareness of self-directed learning and lifelong learning, with the ability to continuously learn and adapt to development.
四、学时与学分 Hours and Credits
类别 Category | 学时 Hours | 学分 Credits | 比例 Percentage | |
必修课 Required course | 公共基础教育 Public infrastructure | 544 | 29 | 16.71% |
学科门类基础 Basis of discipline | 514 | 32 | 18.44% | |
专业类基础 Basis of major | 616 | 38.5 | 22.19% | |
专业核心 Core of major | 256 | 16 | 9.22% | |
集中实践 Intensive practice |
| 33 | 19.02% | |
必修课小计 Subtotal of Required course | 1930 | 148.5 | 85.59% | |
选修课 Elective courses | 320 | 20 | 11.53% | |
课外实践学分 Practice of extra-curricular |
| 5 | 2.88% | |
总计Total | 2250 | 173.5 | 100% |
五、专业主干课程 Main Courses
人工智能基础、脑与认知科学基础、智能电网导论、智能控制导论、智能电网信息安全技术、电力设备状态智能感知、模式识别、智能信息处理、电力大数据分析与应用、机器学习、机器人学、网络化群体智能、自然语言处理、机器视觉、智能优化及应用、智能传感器网络、分子计算、纳米智能机器、量子计算、智慧能源系统概论、深度学习、智能科学与技术前沿讲座。
Artificial Intelligence Foundation, Brain and Cognitive Science Foundation, Smart Grid Introduction, Intelligent Control Introduction, Smart Grid Information Security Technology, Power Device State IntelliSense, Pattern Recognition, Intelligent Information Processing, Big Data Analysis and Application in Electric Power Industry, Machine Learning, Robotics, Networked Group Intelligence, Natural Language Processing, Computer Vision, Intelligent optimization and application, Intelligent sensor networks, Molecular computing, Nano-intelligent machines, Quantum computing, Intelligent energy systems, Deep learning, Intelligent science and technology frontier lectures.
六、总周数分配 Arrangement of the Total Weeks
总 周 数 分 配
学期Semester 教学环节Teaching Program | 一 | 二 | 三 | 四 | 五 | 六 | 七 | 八 | 合计 |
理论教学 Theoretic Teaching | 16 | 16 | 17 | 17 | 16 | 17 | 17 |
| 116 |
复习考试Review and Exam | 1 | 2 | 1 | 2 | 1 | 2 | 2 |
| 11 |
集中实践环节Intensive practice | 3 | 3 | 3 | 2 | 4 | 2 | 0 | 18 | 35 |
小计Subtotal | 20 | 21 | 21 | 21 | 21 | 21 | 19 | 18 | 162 |
寒假Winter Vacation | 5 |
| 5 |
| 5 |
| 5 |
| 20 |
暑假Summer Vacation |
| 6 |
| 6 |
| 6 |
|
| 18 |
合计Total | 25 | 26 | 26 | 27 | 27 | 27 | 24 | 19 | 200 |
智能科学与技术专业必修课程体系及教学计划
Table of Teaching Schedule for Required Course and Teaching Plan
类 别 | 课程编号 | 课程名称 | 学 分 | 总 学时 | 课内 学时 | 实验 学时 | 上机 学时 | 课外 学时 | 开课 学期 | 必修 选修 |
公共基础教育 | 00701351 | 思想道德修养与法律基础 Ideology and Moral Cultivation & Law Basis | 3 | 48 | 32 |
|
| 16 | 1 | 必修 17 |
00700975 | 中国近现代史纲要 Chinese Modern and Contemporary History Outline | 3 | 48 | 32 |
|
| 16 | 2 | ||
00700981 | 毛泽东思想和中国特色社会主义理论体系概论 Mao Zedong Thought and the theory of building socialism with Chinese characteristics | 5 | 80 | 56 |
|
| 24 | 3 | ||
00700971 | 马克思主义基本原理 Marxism Basic Principle | 3 | 48 | 32 |
|
| 16 | 1 | ||
00701650 | 形势与政策 Current Affair and Policy | 2 | 32 | 12 |
|
| 20 | 1-8 | ||
01390011 | 军事理论 Military theory | 1 | 16 | 16 |
|
|
| 1 | ||
00801410 | 通用英语 English for General Purpose | 4 | 64 | 48 |
| 16 |
| 1 | 必修 8 | |
00801400 | 学术英语 English for Academic Purpose | 4 | 64 | 64 |
|
|
| 2 | ||
01000011 | 体育(1) Physical Culture (1) | 1 | 36 | 30 |
|
| 6 | 1 | 必修 4 | |
01000021 | 体育(2) Physical Culture (2) | 1 | 36 | 30 |
|
| 6 | 2 | ||
01000031 | 体育(3) Physical Culture (3) | 1 | 36 | 30 |
|
| 6 | 3 | ||
01000041 | 体育(4) Physical Culture (4) | 1 | 36 | 30 |
|
| 6 | 4 | ||
公共基础教育小计Subtotal of public infrastructure | 必修29 | |||||||||
学 科 门 类 基 础 课 | 00900130 | 高等数学B(1) Advanced Mathematics B(1) | 5.5 | 90 | 90 |
|
|
| 1 | 必修 |
00900140 | 高等数学B(2) Advanced Mathematics B(2) | 6 | 96 | 96 |
|
|
| 2 | ||
00900462 | 线性代数 Linear Algebra | 3 | 48 | 48 |
|
|
| 3 | ||
00900111 | 概率论与数理统计B Probability and Mathematical Statistics B | 3.5 | 56 | 56 |
|
|
| 4 | ||
00900053 | 大学物理(1) College Physics(1) | 3.5 | 56 | 56 |
|
|
| 2 | ||
00900064 | 大学物理(2) College Physics(2) | 3 | 48 | 48 |
|
|
| 3 | ||
00900440 | 物理实验(1) Experiments of Physics(1) | 2 | 32 |
| 32 |
|
| 2 | ||
00900450 | 物理实验(2) Experiments of Physics(2) | 2 | 32 |
| 32 |
|
| 3 | ||
00600200 | 高级语言程序设计(C) Advanced Language Programming(C) | 3.5 | 56 | 30 |
| 26 |
| 1 | ||
学科门类基础课小计subtotal of basis of discipline | 必修32 | |||||||||
专 业 类 基 础 课 | 00600261 | 计算机导论 Introduction to Computer Science | 0.5 | 8 | 8 |
|
|
| 1 | 必修 |
00600460 | 离散数学 Discrete Mathematics | 4 | 64 | 64 |
|
|
| 1 | ||
00600603 | 数据结构与算法 Data Structure and Algorithm | 4 | 64 | 64 |
|
|
| 2 | ||
00600491 | 面向对象的程序设计(Java) Object-Oriented Programming (Java) | 3.5 | 56 | 56 |
|
|
| 3 | ||
00601400 | 数据分析与程序设计(python / R) Data Analysis and Programming | 2 | 32 | 32 |
|
|
| 4 | ||
00500410 | 数字逻辑与数字系统设计 Digital Logic and Digital System Design | 3 | 48 | 48 |
|
|
| 4 | ||
00600101 | 操作系统A Operating Systems A | 4 | 64 | 56 |
| 8 |
| 4 | ||
00601410 | 人工智能基础 Foundation of Artificial Intelligence | 2 | 32 | 32 |
|
|
| 4 | ||
00601420 | 脑与认知科学基础 Brain and Cognitive Science Foundation | 2 | 32 | 32 |
|
|
| 4 | ||
00600411 | 计算机组成与结构 Computer Architecture | 4 | 64 | 48 | 16 |
|
| 5 | ||
00400700 | 自动控制理论A Automation Control Theory A | 4 | 64 | 60 | 4 |
|
| 5 | ||
10410160 | 计算机网络 Computer network | 3 | 48 | 48 |
|
|
| 6 | ||
00600621 | 数据库原理 Principles of Database | 2.5 | 40 | 40 |
|
|
| 5 | ||
专业类基础课小计subtotal of basis of major | 必修38.5 | |||||||||
专 业 类 核 心 课 | 00601440 | 机器学习(上) Machine learning (1) | 2 | 32 | 32 |
|
|
| 5 | 必修 |
00601450 | 智能控制导论 Introduction of Intelligent Control | 2.5 | 40 | 40 |
|
|
| 5 | ||
00601460 | 智能信息处理 Information Processing | 2 | 32 | 32 |
|
|
| 6 | ||
00601470 | 模式识别 Pattern Recognition | 3 | 48 | 48 |
|
|
| 6 | ||
00601480 | 机器学习(下) Machine learning (2) | 2 | 32 | 32 |
|
|
| 6 | ||
00601490 | 机器人学 Robotics | 2.5 | 40 | 32 | 8 |
|
| 7 | ||
00601500 | 电力大数据分析与应用 Big Data in Electric Power | 2 | 32 | 26 |
| 6 |
| 7 |
| |
专业核心课小计 Subtotal of Core of major | 必修16 | |||||||||
| 必修课程学分小计Subtotal of Required course | 115.5 |
智能科学与技术专业选修课程体系及教学计划
Table of Teaching Schedule for Elective Course and Teaching Plan
类 别 | 课程编号 | 课程名称 | 学 分 | 总 学时 | 课内 学时 | 实验 学时 | 上机 学时 | 课外 学时 | 开课 学期 | 必修 选修 | |
选 修 课 | 00201980 | 理论模块 | 智能电网导论 Introduction of Smart Grid | 2 | 32 | 32 |
|
|
| 4 | 至少 选修 20 学分
|
00601510 | 智能电网信息安全技术 Information Security Technology of Smart Grid | 2 | 32 | 32 |
|
|
| 5 | |||
00601520 | 形式化方法 Formal Method | 2 | 32 | 32 |
|
|
| 5 | |||
00601530 | 数理逻辑 Mathematical Logic | 2 | 32 | 32 |
|
|
| 5 | |||
00601660 | 分子计算 Molecular computing | 2 | 32 | 32 |
|
|
| 5 | |||
00601540 | 深度学习 Deep Learning | 2 | 32 | 32 |
|
|
| 6 | |||
00601550 | 智慧能源系统概论 Introduction of The Intelligent Energy System | 2 | 32 | 32 |
|
|
| 7 | |||
00601560 | 应用模块 | 自然语言处理 Natural language Processing | 2 | 32 | 32 |
|
|
| 6 | ||
00601570 | 机器视觉 Computer Vision | 2 | 32 | 32 |
|
|
| 6 | |||
00601580 | 智能优化及应用 Intelligent Optimization and Application | 2 | 32 | 32 |
|
|
| 6 | |||
00601590 | 网络化群体智能 Network Group Intelligence | 2 | 32 | 32 |
|
|
| 6 | |||
00601600 | 纳米智能机器人 Nano Intelligent Robot | 2 | 32 | 32 |
|
|
| 6 | |||
00601610 | 电力设备状态智能感知 Intelligent Perception of Electric Power Equipment State | 2 | 32 | 32 |
|
|
| 7 | |||
00601620 | 量子计算 Quantum Computation | 2 | 32 | 32 |
|
|
| 7 | |||
00601630 | 智能传感器网络 Intelligent Sensor Network | 2 | 32 | 32 |
|
|
| 7 | |||
00601640 | 通识模块 | 专业英语(智能科学与技术) Specialty English | 2 | 32 | 32 |
|
|
| 5 | ||
00601650 | 智能科学与技术前沿讲座 Lecture on Advanced Intelligent Science and Technology | 1 | 16 | 16 |
|
|
| 7 | |||
通识教育选修课程General knowledge electives | 建议 | ||||||||||
跨专业课程 Cross-major Electives | 建议 | ||||||||||
研究生学位课程 Postgraduate Electives | 建议 | ||||||||||
选修小计 Subtotal of Electives | 至少选修20学分 |
智能科学与技术专业集中实践环节设置及教学计划
Table of Teaching Schedule for Main Practical Training
类别 | 课序号 | 环节名称 | 学分 | 周数 | 学时数 | 开课 学期 |
必修 |
集 中 实 践 | 01390012 | 军事实践 Military Training | 2 | 2 |
| 1 | |
00690270 | C语言课程设计 Course Project of Advanced Language Programming(C) | 2 | 2 |
| 1 | ||
00690130 | 计算机认识实习 Acquaintanceship Practice of Computer | 1 | 1 |
| 3 | ||
| 数据结构与算法课程设计 Course Project of Data Structure and Algorithm | 2 | 2 |
| 2 | ||
00490090 | 公益劳动 Public Laboring | 1 | (1) |
| 3 | ||
00690060 | 操作系统课程设计 Course Project of Operating System | 1 | 1 |
| 4 | ||
00690740 | 数据分析与程序设计(python / R) 课程设计 Course Project of Data Analysis and Program Design | 1 | 1 |
| 4 | ||
00690190 | 数据库应用课程设计 Course Project of Database Application | 1 | 1 |
| 5 | ||
00490240 | 自动控制理论课程设计 Course Project of Automation Control Theory | 1 | 1 |
| 5 | ||
00690750 | 机器学习课程设计 Course Project of Machine Learning | 2 | 2 |
| 6 | ||
00690290 | 计算机网络实验 Experiments of Computer Networks | 1 | 1 |
| 6 | ||
00690760 | 智能机器人课程设计 Course Project of Intelligent Robot | 2 | 2 |
| 7 | ||
00690770 | 电力大数据分析与应用课程设计 Course Project of Big Data Analysis and Application in Electric Power | 1 | 1 |
| 7 | ||
00690031 | 毕业实习 Graduation Internship | 2 | 2 |
| 8 | ||
00690021 | 毕业设计 Graduation Thesis | 13 | 13 |
| 8 | ||
00690010 | 毕业教育 Graduation Education |
| 1 |
| 8 | ||
集中实践小计Subtotal of intensive practice | 必修33 |
智能科学与技术专业分学期教学进程
第一学年 | ||||||||||||||||||
第一学期 | 第二学期 | |||||||||||||||||
课程 | 课程编号 | 课程名称 | 学分 | 课程 | 课程 | 课程编号 | 课程名称 | 学分 | 课程 | |||||||||
必修 | 00701650 | 形势与政策 | 0.25 | 理论 | 必修 | 00701650 | 形势与政策 | 0.25 | 理论 | |||||||||
00701351 | 思想道德修养与法律基础 | 3 | 00700972 | 中国近代史纲要 | 3 | |||||||||||||
00700971 | 马克思主义原理 | 3 | 00801400 | 学术英语 | 4 | |||||||||||||
01390011 | 军事理论 | 1 | 00900140 | 高等数学B(2) | 6 | |||||||||||||
00900130 | 高等数学B(1) | 5.5 | 00900053 | 大学物理(1) | 3.5 | |||||||||||||
00801410 | 通用英语 | 4 | 00600603 | 数据结构与算法 | 4 | |||||||||||||
00600261 | 计算机导论 | 0.5 | 01000021 | 体育(2) | 1 | |||||||||||||
00600200 | 高级语言程序设计(C) | 3.5 |
|
|
| |||||||||||||
00600460 | 离散数学 | 4 |
|
|
| |||||||||||||
01000011 | 体育(1) | 1 |
|
|
| |||||||||||||
01390012 | 军事实践 | 2 | 实践 |
| 数据结构与算法课程设计 | 2 | 实践 | |||||||||||
00690270 | C语言课程设计 | 2 | 00900440 | 物理实验(1) | 2 | |||||||||||||
必修学分小计 | 29.75 |
| 必修学分小计 | 25.75 |
| |||||||||||||
第二学年 | ||||||||||||||||||
第三学期 | 第四学期 | |||||||||||||||||
课程 | 课程编号 | 课程名称 | 学分 | 课程 | 课程 | 课程编号 | 课程名称 | 学分 | 课程 | |||||||||
必修 | 00701650 | 形势与政策 | 0.25 | 理论 | 必修 | 00701650 | 形势与政策 | 0.25 | 理论 | |||||||||
00700981 | 毛泽东思想和中国特色社会主义理论体系概论 | 5 | 00601400 | 数据分析与程序设计(python / R) | 2 | |||||||||||||
00900462 | 线性代数 | 3 | 00600651 | 数字逻辑与数字系统设计 | 3 | |||||||||||||
00900064 | 大学物理(2) | 3 | 00601410 | 人工智能基础 | 2 | |||||||||||||
00600491 | 面向对象程序设计(Java) | 3.5 | 00601420 | 脑与认知科学基础 | 2 | |||||||||||||
01000031 | 体育(3) | 1 | 00600101 | 操作系统A | 4 | |||||||||||||
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| 00900111 | 概率论与数理统计B | 3.5 | |||||||||||||
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| 01000041 | 体育(4) | 1 | |||||||||||||
00900450 | 物理实验(2) | 2 | 实践 | 00690060 | 操作系统课程设计 | 1 | 实践 | |||||||||||
00490090 | 公益劳动 | 1 | 00690740 | 数据分析与程序设计(python / R)课程设计 | 1 | |||||||||||||
00690130 | 计算机认识实习 | 1 |
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必修学分小计 | 19.75 |
| 必修学分小计 | 19.75 |
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选修 专业 模块 |
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| 选修 专业 模块 | 00201980 | 智能电网导论 | 2 |
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第三学年 | ||||||||||||||||||
第五学期 | 第六学期 | |||||||||||||||||
课程 | 课程编号 | 课程名称 | 学分 | 课程 | 课程 | 课程编号 | 课程名称 | 学分 | 课程 | |||||||||
必修 | 00701650 | 形势与政策 | 0.25 | 理论 | 必修 | 00701650 | 形势与政策 | 0.25 | 理论 | |||||||||
00400700 | 自动控制理论A | 4 | 10410160 | 计算机网络 | 3 | |||||||||||||
00600411 | 计算机组成与结构 | 4 | 00601480 | 机器学习(下) | 2 | |||||||||||||
00600621 | 数据库原理 | 2.5 | 00601470 | 模式识别 | 3 | |||||||||||||
00601450 | 智能控制导论 | 2.5 | 00601460 | 智能信息处理 | 2 | |||||||||||||
00601440 | 机器学习(上) | 2 | 00690290 | 计算机网络实验 | 1 | |||||||||||||
00490240 | 自动控制理论课程设计 | 1 | 实践 | 00690750 | 机器学习课程设计 | 2 | 实践 | |||||||||||
00690190 | 数据库应用课程设计 | 1 |
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必修学分小计 | 17.25 |
| 必修学分小计 | 13.25 |
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选修 专业 模块 | 00601510 | 智能电网信息安全技术 | 2 |
| 选修 专业 模块 | 00601540 | 深度学习 | 2 |
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00601660 | 分子计算 | 2 | 00601570 | 机器视觉 | 2 | |||||||||||||
00601640 | 专业英语(智能科学与技术) | 2 | 00601580 | 智能优化及应用 | 2 | |||||||||||||
00601520 | 形式化方法 | 2 | 00601600 | 纳米智能机器人 | 2 | |||||||||||||
00601530 | 数理逻辑 | 2 | 00601560 | 自然语言处理 | 2 | |||||||||||||
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| 00601590 | 网络化群体智能 | 2 | |||||||||||||
第四学年 | ||||||||||||||||||
第七学期 | 第八学期 | |||||||||||||||||
课程 | 课程编号 | 课程名称 | 学分 | 课程 | 课程 | 课程编号 | 课程名称 | 学分 | 课程 | |||||||||
必修 | 00701650 | 形势与政策 | 0.25 | 理论 | 必修 | 00701650 | 形势与政策 | 0.25 | 理论 | |||||||||
00601490 | 机器人学 | 2.5 |
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00601500 | 电力大数据分析与应用 | 2 |
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00690760 | 智能机器人课程设计 | 2 | 实践 | 00690030 | 毕业实习 | 2 | 实践 | |||||||||||
00690770 | 电力大数据分析与应用课程设计 | 1 | 00690020 | 毕业设计 | 13 | |||||||||||||
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| 00690010 | 毕业教育 |
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必修学分小计 | 7.75 |
| 必修学分小计 | 15.25 |
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选修 专业 模块 | 00601610 | 电力设备状态智能感知 | 2 |
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00601620 | 量子计算 | 2 |
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00601550 | 智慧能源系统概论 | 2 |
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00601630 | 智能传感器网络 | 2 |
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00601650 | 智能科学与技术前沿讲座 | 1 |
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