上海交通大学国际与公共事务学院第五届“暑期社会科学方法论培训班”机器学习部分课程安排
王成军
Note: 本部分基于python介绍机器学习的基本逻辑和算法，需要学员提前安装anaconda）。
| 序号 | 日期 | 时间 | 内容 | 课时数量 |
|---|---|---|---|---|
| 1 | 6月19日 周三 | 9:00-12:00 新建楼319多功能厅 | 机器学习简介：从泰坦尼克号讲起 | 3 |
| 2 | 6月20日 周四 | 9:00-12:00 新建楼319多功能厅 | 机器学习初步: 朴素贝叶斯与线性回归 | 3 |
| 3 | 6月20日 周四 | 13:30-16:30 新建楼319多功能厅 | 机器学习进阶: 支持向量机与随机森林 | 3 |
| 4 | 6月21日 周五 | 9:00-12:00 新建楼319多功能厅 | 机器学习扩展: 基于Pytorch的神经网络模型 | 3 |
https://github.com/computational-class/machine-learning
- What Is Machine Learning?
- Introducing Scikit-Learn
- Hyperparameters and Model Validation
- Feature Engineering
- In Depth: Naive Bayes Classification
- In Depth: Linear Regression
- In-Depth: Support Vector Machines
- In-Depth: Decision Trees and Random Forests
- In Depth: Neural Networks
- In Depth: Neural Networks Advanced
- 房价预测 https://www.kaggle.com/c/house-prices-advanced-regression-techniques/
- 预测银行用户是否参与定期存款 http://www.dcjingsai.com/common/cmpt/ANZ%20Chengdu%20Data%20Science%20Competition_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html?lang=en_US
- 游戏玩家的付费预测 http://www.dcjingsai.com/common/cmpt/%E6%B8%B8%E6%88%8F%E7%8E%A9%E5%AE%B6%E4%BB%98%E8%B4%B9%E9%87%91%E9%A2%9D%E9%A2%84%E6%B5%8B%E5%A4%A7%E8%B5%9B_%E7%AB%9E%E8%B5%9B%E4%BF%A1%E6%81%AF.html
- 预测假新闻 https://www.kaggle.com/c/fake-news
- Whirlwind Tour Of Python https://jakevdp.github.io/WhirlwindTourOfPython/
- Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/
- Python for Data Analysis by Wes McKinney, published by O'Reilly Media https://github.com/data-science-lab/pydata-book
- Introduction to Machine Learning with Python: A Guide for Data Scientist https://github.com/amueller/introduction_to_ml_with_python.
- Machine Learning in Action https://github.com/pbharrin/machinelearninginaction & https://github.com/RedstoneWill/MachineLearningInAction-Camp & https://github.com/TingNie/Machine-learning-in-action
- 周志华《机器学习》,北京:清华大学出版社,2016. (ISBN 978-7-302-42328-7)
- Easley, David and Jon Kleinberg. 2011.Networks, Crowds, and Markets: Reasoning About a Highly Connected World. New York: Cambridge University. 大卫・伊斯利, & 乔恩・克莱因伯格. (2011). 网络、群体与市场:揭示高度互联世界的行为原理与效应机制. 清华大学出版社.
- Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman.2011. Mining massive datasets (2nd)http://www.mmds.org/
- 南京大学《大数据挖掘与分析》课程 https://github.com/computational-class/bigdata
- 用Python玩转数据_**大学MOOC(慕课) http://www.icourse163.org/course/nju-1001571005
- Advanced Machine Learning with scikit-learn, Andreas Müller http://bit.ly/advanced_machine_learning_scikit-learn & https://github.com/computational-class/PythonMachineLearning & **https://www.bilibili.com