# machine-learning

**Repository Path**: guanwl/machine-learning

## Basic Information

- **Project Name**: machine-learning
- **Description**: Machine Learning for the Social Scientist
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-05
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Machine Learning for the Social Scientist

王成军

**社会科学家的机器学习（Machine Learning for the Social Scientist)** 工作坊简介

本工作坊面向社会科学研究者，采用Python介绍机器学习（和深度学习）的基本逻辑（需要学员提前安装Anaconda），主要内容包括三（或四）个部分：1. 机器学习简介：从泰坦尼克号讲起；2. 机器学习初步: 朴素贝叶斯与线性回归；3. 机器学习进阶：支持向量机与随机森林；4. 机器学习扩展：基于Pytorch的神经网络模型（备选）。本工作坊所使用到的Slides、Python代码、阅读文献、案例见 https://github.com/computational-class/machine-learning


> 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、熟悉Jupyter notebook的使用、安装pytorch）。




| 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

### Slides

- [What Is Machine Learning?](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.01-What-Is-Machine-Learning.ipynb#/)
- [Introducing Scikit-Learn](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.02-machine-learning-with-sklearn.ipynb#/)
- [Hyperparameters and Model Validation](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.03-Hyperparameters-and-Model-Validation.ipynb#/)
- [Feature Engineering](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.04-Feature-Engineering.ipynb#/)
- [In Depth: Naive Bayes Classification](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.05-Naive-Bayes.ipynb#/)
- [In Depth: Linear Regression](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.06-Linear-Regression.ipynb#/)
- [In-Depth: Support Vector Machines](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.07-Support-Vector-Machines.ipynb#/)
- [In-Depth: Decision Trees and Random Forests](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.08-Random-Forests.ipynb#/)
- [In Depth: Neural Networks 1](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.09.neural_network_1.ipynb#/)
- [In Depth: Neural Networks 2](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.09.neural_network_2.ipynb#/)
- [In Depth: Neural Networks 3](https://nbviewer.jupyter.org/format/slides/github/computational-class/machine-learning/blob/master/09.09.neural_network_pytorch.ipynb#/)

## 案例
- 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

## 推荐教材

- **Whirlwind Tour Of Python** https://jakevdp.github.io/WhirlwindTourOfPython/
- **Python Data Science Handbook** https://jakevdp.github.io/PythonDataScienceHandbook/

## 参考书
- **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](http://www.cs.cornell.edu/home/kleinber/). 2011.[Networks, Crowds, and Markets: Reasoning About a Highly Connected World](http://www.cs.cornell.edu/home/kleinber/networks-book/). New York: Cambridge University. 大卫・伊斯利, & 乔恩・克莱因伯格. (2011). [网络、群体与市场](https://www.baidu.com/s?wd=%E7%BD%91%E7%BB%9C%E3%80%81%E7%BE%A4%E4%BD%93%E4%B8%8E%E5%B8%82%E5%9C%BA):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/


## 相关课程

- 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_中国大学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