{"id":20673014,"url":"https://github.com/relph1119/machinelearning-watermelonbook","last_synced_at":"2025-07-31T06:06:11.268Z","repository":{"id":63172393,"uuid":"177908322","full_name":"Relph1119/MachineLearning-WatermelonBook","owner":"Relph1119","description":"周志华-机器学习","archived":false,"fork":false,"pushed_at":"2020-04-16T02:50:44.000Z","size":92616,"stargazers_count":276,"open_issues_count":0,"forks_count":65,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-04-10T05:08:16.571Z","etag":null,"topics":["machine-learning","watermelon"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 《机器学习》（周志华-西瓜书）训练营（深度之眼-第十期）\n\n## 课程资料\n- [ApacheCN](http://ml.apachecn.org/mlia/)\n- [廖雪峰 Python3 教程](https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000)\n- 周志华《机器学习》(链接：https://pan.baidu.com/s/1KRVO694H3QSP8jAqdegzHA 密码：gwpt)\n\n- 优秀github资源：\n[周志华《机器学习》学习笔记](https://github.com/Vay-keen/Machine-learning-learning-notes)  \n[周志华《机器学习》课后参考答案](https://github.com/Tsingke/Machine-Learning_ZhouZhihua)\n[周志华《机器学习》课后参考答案-博客](https://www.cnblogs.com/tsingke/p/7233399.html)\n- 本训练营的学习安排与课程任务：详见文件夹Books中的《机器学习作业》doc文档\n\n## 课程安排\n**总课时：11 周**\n\n### 第一周\n- 1 学习机器学习绪论\n- 2 达观杯NLP算法大赛\n\n### 第二周\n- 3 学习线性模型\n- 4 学习sklearn包中逻辑回归算法的使用\n\n### 第三周\n- 5 决策树的分裂准则\n- 6 决策树的剪枝和连续值处理\n- 7 学习sklearn包中决策树算法的使用\n\n### 第四周\n- 8 支持向量机原始模型的建立和求解\n- 9 核函数和软间隔支持向量机\n- 10 了解sklearn包中svm算法的使用\n\n### 第五周\n- 11 极大似然估计与朴素贝叶斯\n- 12 EM算法\n- 13 了解sklearn包中的朴素贝叶斯算法的适用\n\n### 第六周\n- 14 神经网络结构\n- 15 BP算法\n- 16 深度学习初探\n- 17 了解sklearn包中神经网络的使用\n\n### 第七周\n- 18 经验误差与过拟合\n- 19 评估方法\n- 20 性能度量\n- 21 了解sklearn包中模型评估方法的使用\n\n### 第八周\n- 22 特征降维\n- 23 特征选择\n- 24 了解sklearn包中特征选择和降维算法的使用\n\n### 第九周\n- 25 集成学习\n- 26 结合策略\n- 27 实验-lightGBM的使用\n\n### 第十周\n- 28 聚类\n- 29 HMM\n- 30 了解sklearn包中K-means算法的使用\n\n### 第十一周\n- 31 K-摇臂赌博机和天池o2o比赛初级\n- 32 有/无模型学习和天池o2o比赛进阶\n\n## 项目目录\n\u003cpre\u003e\nBooks----------------------------------作业汇总和西瓜书笔记pdf文档\nNote-----------------------------------笔记文件夹\n+----image-----------------------------笔记截图\n+----markdown--------------------------markdown格式视频笔记\n+----notebook--------------------------JupyterNotebook格式视频笔记\nWeek1----------------------------------第一周作业\nWeek2----------------------------------第二周作业\nWeek3----------------------------------第三周作业\nWeek4----------------------------------第四周作业\nWeek5----------------------------------第五周作业\nWeek6----------------------------------第六周作业\nWeek7----------------------------------第七周作业\nWeek8----------------------------------第八周作业\nWeek9----------------------------------第九周作业\nWeek10---------------------------------第十周作业\nWeek11---------------------------------第十一周作业\n\u003c/pre\u003e\n\n## 总结\n\u0026emsp;\u0026emsp;前后用了一周时间结合Vay-keen大神的笔记做的整理，首先感谢Vay-keen大神为我们这些学习者节约了寻找相关辅助学习资料的时间，笔记中有很多相关的知识是书中没有的，同时笔者也加入了一些其他的辅助公式推导，在此也要感谢南瓜书的作者，是他们的开源公式推导帮助了我们。  \n\u0026emsp;\u0026emsp;在学这本书之前，笔者曾经学了李航老师的第一版《统计学习方法》和《机器学习实战》，当时对里面一些公式部分不是很理解，这次系统性地学习了一遍西瓜书，结合了《统计学习方法》中的例题，更加深入的理解了机器学习中这些经典的算法，为接下来的《百面》一书的学习打下基础。笔者整理出了西瓜书笔记的PDF版本，供各位学习者下载使用。","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frelph1119%2Fmachinelearning-watermelonbook","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frelph1119%2Fmachinelearning-watermelonbook","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frelph1119%2Fmachinelearning-watermelonbook/lists"}