feat: postgraduate docx
--------- Co-authored-by: Tian-yi_Liang <41582525+tianyilt@users.noreply.github.com> Co-authored-by: oceanlvr <657531018@qq.com> Co-authored-by: Sinphone <49327032+Sinphone@users.noreply.github.com> Co-authored-by: Laptop Go <zee_lin@foxmail.com> Co-authored-by: Kausal-Lei <65400838+Kausal-Lei@users.noreply.github.com> Co-authored-by: Kale1d0 <kale1d0@qq.com>
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title: 人工智能+教育创新应用实践
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---
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- [作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E5%85%AC%E5%85%B1%E8%AF%BE/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD+%E6%95%99%E8%82%B2%E5%88%9B%E6%96%B0%E5%BA%94%E7%94%A8%E5%AE%9E%E8%B7%B5/%E4%BD%9C%E4%B8%9A)
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---
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title: 学术英语
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---
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- [资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E5%85%AC%E5%85%B1%E8%AF%BE/%E5%AD%A6%E6%9C%AF%E8%8B%B1%E8%AF%AD)
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---
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title: 思想政治与实践
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---
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- [考试情报](https://drive.vanillaaaa.org/SharedCourses/postgraduate/公共课/思想政治与实践/考试情报.pdf)
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- [考试打印](https://drive.vanillaaaa.org/SharedCourses/postgraduate/公共课/思想政治与实践/考试打印.docx)
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## 考试情报
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时间2021年 11月24日下午1点-3点
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考场待定,等研究生教学秘书通知.
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材料分析题2选1论述题4选2. 前9章只有一个问题不会直接讲,但是pre的时候覆盖掉.两个论述题完整答案呈现过.
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考试卷上问题几乎都在前5章,覆盖了其中五道题,合理猜测一章一道题
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21版教材支持手写笔记 拍下来的在课本上整理
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复印笔记不被允许,会被清算
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电子设备不要带在身上
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考试时身份证/校园卡/研究生证三选
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## 后记
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1. 论述题四题中有3题在书上课后题,其中两题在第一章,所以可以看看书后答案
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2. 老师上课画重点参考意义不大,比如上课提到的供给侧改革,明确指出考法,而且放了很多页在书上找不到内容的ppt,结果半点没考,不排除放到B卷了,反正押题有风险不如不复习。
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经济那一章讲了好几节课,就跟就在材料分析题一个体现了,而且完全没打中重点。
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3. 考试成绩对评奖没有作用.
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## 课堂
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上课在9点半前不能看电脑,可以考虑用手机或者带有键盘的平板做自己的事情
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点名是扫码,好像每几S就刷新二维码,靠别人点名,建议发送方建议开视频,接收方坐在电脑前然后手机扫码
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---
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title: 量子计算导论
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---
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- [资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E5%85%AC%E5%85%B1%E8%AF%BE/%E9%87%8F%E5%AD%90%E8%AE%A1%E7%AE%97%E5%AF%BC%E8%AE%BA)
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课程名称:量子计算导论(公共选修课)
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任课教师:[邓玉欣](https://faculty.ecnu.edu.cn/_s43/dyx/main.psp)(前半部分,基础知识)、[徐鸣](https://faculty.ecnu.edu.cn/_s43/xm2/main.psp)(后半部分,两个主要算法)
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课程内容:<del>线性代数与概率论综合应用课。</del>量子计算的代数基础与最著名的量子算法。
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课堂评价:教师有编写自研教材,助教会发纸质版。上课不点名,以讲课为主,无互动。作业量不少,课程内容对线性代数苦手不太友好。附上了部分作业的个人答案,不保证正确性,仅供参考。
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考核方式:作业决定平时成绩,通过大夏学堂提交,期末有开卷笔试考核。附上了当年的考试试卷。
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---
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title: 专硕_智能金融技术
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---
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- [课件](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF/%E4%B8%93%E7%A1%95_%E6%99%BA%E8%83%BD%E9%87%91%E8%9E%8D%E6%8A%80%E6%9C%AF/%E8%AF%BE%E4%BB%B6)
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任课老师:[Chenhui Li](http://chenhui.li/)
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介绍的工具list:
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- 预测
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- [huseinzol05/Stock-Prediction-Models: Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations (github.com)](https://github.com/huseinzol05/Stock-Prediction-Models)
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- 量化
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- [JoinQuant聚宽量化交易平台](https://www.joinquant.com/)
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- [N视界量化投资:集投资研究、策略发现、组合优化、资产配置、业绩评估以及归因分析等服务 (n-sight.com.cn)](https://www.n-sight.com.cn/u/w2/index/index.html?viewMode=pro#KS1648282693756)
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---
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title: 专硕_高级计算机系统结构
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---
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- [资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF/%E4%B8%93%E7%A1%95_%E9%AB%98%E7%BA%A7%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%B3%BB%E7%BB%9F%E7%BB%93%E6%9E%84)
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title: 学硕_机器学习中的优化算法
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---
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详情参考课程网站: [点我](https://chengchen8.github.io/optml2023.html)
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---
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title: 学硕_深度学习与人工智能
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---
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- [大作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_深度学习与人工智能/深度学习大作业.pptx)
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title: 学硕_现代计算机网络
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---
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- [所有资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_现代计算机网络)
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上课方式:
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- 总体还是推荐选。上课可以开电脑,节约时间做科研。
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- 学的内容之前有基础,还有考前提供与测试集高相关的材料。
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- 英文课件中文讲解
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考试:
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- 考试内容都在复习ppt和复习讲义上。
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- 但是会对题目进行扰动,因此这并不是重建任务,只要train一发来overfit training dataset就行了,而是out-of-distribution synthesis任务,要搞懂题目的原理。
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- 题型:
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- 第一大题8个小题,描述题,会涉及到复习ppt的知识点描述
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- 之后若干计算题到结束,数字会有扰动
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- 卷子有中文英文两种,题目一样。看大家对哪种比较熟悉。
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---
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title: 学硕_计算理论基础
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---
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- [讲义](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_计算理论基础/讲义)
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---
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title: 学硕_高级机器学习
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---
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授课教师:赵静
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- [作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_高级机器学习/作业)
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- [课件](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_高级机器学习/课件)
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教师授课 (前 13 周 ) + 学生分享( 后 4 周 )
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考核:平时成绩 40%+ 最终报告 60%
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平时成绩
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按小组介绍 2021 年 ICML 、 NeurIPS 、 IJCAI 、 AAAI Tutorial
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每 组 45 分钟,每周两组
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按 小组打分,学生互评
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期末考核:
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机器学习 相关的论文
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按照正规会议期刊格式撰写,提供模板, 6 页上限
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• 综述论文 ( 60-80 分)
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• 近 5 年顶会刊算法 复现,需增加原文之外的数据集( 80-90 分)
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• 创新性论文( 90-100 分)
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第一章 简单机器学习算法概览
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学时:10
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本章节内容概述:回顾模式识别与机器学习基础算法,包括
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1.1贝叶斯决策(2学时),
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1.2 线性分类与回归模型(2学时),
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1.3支持向量机与拉格朗日对偶优化(2学时),
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1.4 EM算法与变分推理(2学时),
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1.5主成分分析与聚类(2学时)
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第二章 高斯过程相关模型²
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学时:6
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本章节内容概述:介绍高斯过程相关模型原理,包括
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2.1高斯过程 (2学时),
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2.2高斯过程潜变量模型 (2学时),
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2.3深度高斯过程和多视图高斯过程 (2学时),
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第三章 概率时序模型²
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学时:4
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本章节内容概述:介绍概率时序模型,包括
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3.1隐马尔科夫模型(2学时),
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3.2条件随机场 (2学时)
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第四章 深度学习模型²
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学时:4
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本章节内容概述:介绍神经网络模型原理及典型的生成式神经网络,包括
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4.1深度神经网络 (2学时),
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4.2变分自编码 (1学时),
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4.3生成式对抗网络 (1学时)
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第5章 近似推理与优化
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学时:4
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本章节内容概述:介绍概率模型的近似推理与基于梯度的随机优化算法,包括
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5.1采样方法 (2学时),
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5.2随机梯度优化(2学时)。
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第六章 学生分享展示
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学时:8
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报告参考范围
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1. [Unsupervised Learning for RL](https://icml.cc/Conferences/2021/Schedule?showEvent=10843)
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2. [Natural-XAI: Explainable AI with Natural Language Explanations](https://icml.cc/Conferences/2021/Schedule?showEvent=10835)
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3. [Self-Attention for Vision](https://icml.cc/Conferences/2021/Schedule?showEvent=10842)
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4. [A Journey Through the Opportunity of Low Resourced Natural Language Processing — An African Lens](https://nips.cc/Conferences/2021/Schedule?showEvent=21898)
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5. [Self-Supervised Learning: Self-Prediction and Contrastive Learning](https://nips.cc/Conferences/2021/Schedule?showEvent=21895)
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6. [The Art of Gaussian Processes: Classical and Contemporary](https://nips.cc/Conferences/2021/Schedule?showEvent=21890)
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7. [Deep Learning for Recommendations: Fundamentals and Advances](https://advanced-recommender-systems.github.io/ijcai2021-tutorial/)
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8. [Learning with Noisy Supervision](https://wsl-workshop.github.io/ijcai21-tutorial#slides)
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9. [Towards Robust Deep Learning Models: Verification, Falsification, and Rectification](https://tutorial-ijcai.trustai.uk/)
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10. [Continual Learning Dialogue Systems - Learning on the Job after Model Deployment](https://www.cs.uic.edu/~liub/IJCAI21-Continual-Learning-Dialogue-Systems-after-Deployment.html)
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11. [NS4NLP-- Neuro-Symbolic methods for Natural Language Processing](https://www.cs.purdue.edu/homes/pachecog/tutorials/ns4nlp/)
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12. [MH2: Commonsense Knowledge Acquisition and Representation](https://usc-isi-i2.github.io/AAAI21Tutorial/)
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13. Dealing with Bias and Fairness in AI/ML/Data Science Systems
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---
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title: 学硕_高级算法
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---
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- [AA1supplement.pptx](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_高级算法/AA1supplement.pptx)
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- [AArandcausal1.pptx](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_高级算法/AArandcausal1.pptx)
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- [CausalInferElem17.pdf](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_高级算法/CausalInferElem17.pdf)
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- [ProbComp2ed](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕_高级算法/ProbComp2ed17.pdf)
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---
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title: 学硕专硕_人机交互
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---
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- [课件](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF/%E5%AD%A6%E7%A1%95%E4%B8%93%E7%A1%95_%E4%BA%BA%E6%9C%BA%E4%BA%A4%E4%BA%92/%E8%AF%BE%E4%BB%B6)
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---
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title: 学硕专硕_具体数学
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---
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- [资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF/%E5%AD%A6%E7%A1%95%E4%B8%93%E7%A1%95_%E5%85%B7%E4%BD%93%E6%95%B0%E5%AD%A6)
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本项目包含《具体数学》课程的 PPT 和 教材部分,每个章节的习题在ppt和教材上均出现过。
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## 练习题答案
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0.教材后面有答案,较详细。
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1.每年的作业题不一样,如有需要联系管理员。
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2.作业题要求电子版,推荐latex。
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## 友情链接
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[阳神的博客](https://godweiyang.com/tags/%E5%85%B7%E4%BD%93%E6%95%B0%E5%AD%A6/)
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[overleaf上的模板](https://www.overleaf.com/articles/extra-credit-problem/dhppczmrnttk)
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---
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title: 学硕专硕_数据可视化
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---
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- [课件, 作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF/%E5%AD%A6%E7%A1%95%E4%B8%93%E7%A1%95_%E6%95%B0%E6%8D%AE%E5%8F%AF%E8%A7%86%E5%8C%96)
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该课程详情见[李晨辉老师的个人主页](http://chenhui.li)
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[研究生课程主页](http://chenhui.li/courses/graduate-datavis2021.html)
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想研习可视化实践技巧,可以参考[本科生的实验材料](http://chenhui.li/courses/datavis2020.html)
|
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|
||||
这两个网页都内部包含课件,实验的材料等,而且对全网用户开放,所以本仓库不另行收录.
|
||||
|
||||
老师讲课很幽默而且水平很高,课程中可以学到很多实用的技巧(此处5毛,括号删掉).
|
||||
|
||||
期末考核形式为项目:
|
||||
大家靠手速和py交易从12个主题里面抢到自己感兴趣的主题然后完成项目.
|
||||
|
||||

|
||||
|
||||

|
||||
|
||||
期末汇报那天,每位同学都要提交一个课程项目报告纸质版,报告模板见作业文件夹。课程报告PPT及项目代码资料的提交由组长完成
|
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---
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||||
title: 学硕专硕_计算机视觉
|
||||
---
|
||||
|
||||
- [资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕专硕_计算机视觉)
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
---
|
||||
title: 学硕专硕_高级工程数学
|
||||
---
|
||||
|
||||
- [资源](https://drive.vanillaaaa.org/SharedCourses/postgraduate/计算机科学与技术/学硕专硕_高级工程数学)
|
||||
|
||||
任课老师: 沈超敏
|
||||
|
||||
计算机科学与技术学院
|
||||
|
||||
教书院116
|
||||
|
||||
## 教科书
|
||||
|
||||
An Introduction to Optimization (Fourth Edition)
|
||||
作者: Edwin K.P. Chong & Stanislaw H. Zak
|
||||
出版社: Wiley, 2013 (电子版)
|
||||
|
||||
## 考核
|
||||
|
||||
有可能被教务抽中期中笔试;
|
||||
期末大概率写论文
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
---
|
||||
title: 学硕_代数形式化方法
|
||||
---
|
||||
|
||||
任课教师:[张民](https://faculty.ecnu.edu.cn/_s43/zm2_6071/main.psp)
|
||||
|
||||
课程内容:使用 Maude 语言以状态搜索为主要方式对常见的问题进行形式化建模与性质验证
|
||||
|
||||
课堂评价:线下授课时会和学生有一定互动,备课较认真,课堂包括编程实操,也会花一些时间苦口婆心跟你讲人生道理。课程成绩按等第给分,给分较宽松。不点名。
|
||||
|
||||
考核方式:期末需要提交约5页的课程报告。附上了一篇满分报告作为示例。
|
||||
|
||||
- [课件](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_代数形式化方法/代数形式化方法.pdf)
|
||||
- [Maude 3.2.1 Manual](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_代数形式化方法/Maude-3.2.1-manual.pdf)
|
||||
- [满分报告](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_代数形式化方法/课程报告-陈实-51255902014.pdf)
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
---
|
||||
title: 学硕_程序验证方法
|
||||
---
|
||||
|
||||
授课老师:[朱惠彪](https://faculty.ecnu.edu.cn/_s43/zhb2/main.psp)
|
||||
|
||||
考核方式:提交报告
|
||||
|
||||
- [教材](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_程序验证方法/教材)
|
||||
- [课件](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_程序验证方法/课件)
|
||||
- [经典论文](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_程序验证方法/经典论文)
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
---
|
||||
title: 学硕_算法分析与设计
|
||||
---
|
||||
|
||||
授课老师:[彭超老师](https://faculty.ecnu.edu.cn/_s43/pc/main.psp)
|
||||
|
||||
好老师!
|
||||
|
||||
- [讲义](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_算法分析与设计/讲义)
|
||||
- [历年试卷](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_算法分析与设计/历年试卷)
|
||||
- [作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕_算法分析与设计/作业)
|
||||
|
||||
## Solutions
|
||||
|
||||
- [Introduction to Algorithms 3E](https://github.com/walkccc/CLRS)
|
||||
|
||||
- [Algorithm Design](https://github.com/mathiasuy/Soluciones-Klenberg)
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
---
|
||||
title: 学硕专硕_智能系统分析与验证
|
||||
---
|
||||
|
||||
授课老师:[李建文老师](https://faculty.ecnu.edu.cn/_s43/ljw2/main.psp)
|
||||
|
||||
好老师!
|
||||
|
||||
- [讲义](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕专硕_智能系统分析与验证/讲义)
|
||||
- [cdcl-example.pdf](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕专硕_智能系统分析与验证/cdcl-example.pdf)
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
---
|
||||
title: 学硕专硕_软件理论基础
|
||||
---
|
||||
|
||||
授课老师:[张敏老师](https://faculty.ecnu.edu.cn/_s43/zm2/main.psp)和[卜天明老师](https://faculty.ecnu.edu.cn/_s43/btm/main.psp)
|
||||
|
||||
- [作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕专硕_软件理论基础/作业)
|
||||
- [讲义](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕专硕_软件理论基础/讲义)
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
---
|
||||
title: 学硕专硕_软硬件协同设计
|
||||
---
|
||||
|
||||
授课老师:[陈仪香老师](https://faculty.ecnu.edu.cn/_s43/cyx/main.psp)
|
||||
|
||||
好老师!
|
||||
|
||||
- [作业](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕专硕_软硬件协同设计/作业)
|
||||
- [讲义](https://drive.vanillaaaa.org/SharedCourses/postgraduate/软件工程学院/学硕专硕_软硬件协同设计/讲义)
|
||||
Loading…
Reference in New Issue