大数据预处理

Undergraduate Level

课程大纲

课件

作业

数据集合

  • 数据
  • 压缩包含有用于授课的保险公司理赔数据、二手车数据、信用卡诈欺数据。
  • 波士顿房价数据为scikit-learn （版本需低于1.2.0）内置数据，可以使用以下代码获取该数据集。 
    from sklearn.datasets import load_boston

Data Mining and Machine Learning

数据挖掘与机器学习, Graduate Level

  • Course Description:

This course provides an introduction to modern techniques for statistical analysis of complex and massive data. It covers most of the machine learning models in supervised learning and unsupervised learning. Examples of these are linear regression, classification, model selection, regularization, basis expansion, kernel smoothing, random forest, and ensemble learning. Applications are discussed as well as computation and theoretical foundations.

  • Prerequisites:
    • Linear Algebra
    • Probability
    • Basic Knowledge of Statistical Learning (Reference: An Introduction to Statistical Learning. (Download PDF))

Textbook

  • Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2, pp. 1-758). New York: Springer.

Slides

  • Lecture 1: Introduction to Statistical Learning and Machine Learning, Supervised Learning, Unsupervised Learning. (Download Slides)
    • Chapter 1
    • Chapter 2: 2.1 – 2.6
  • Lecture 2: Linear Regression, Model Assessment and Selection. (Download Slides)
    • Chapter 3: 3.1 – 3.3,
    • Chapter 7: 7.1 – 7.5, 7.7
  • Lecture 3: Linear Regression with Derived Input Directions, Shrinkage Methods (Regularization), Cross-Validation, Boostrap. (Download Slides)
    • Chapter 3: 3.4 – 3.5
    • Chapter 7: 7.10, 7.11
  • Lecture 4: Regularization (Lasso, Ridge), Logistic Regression. (Download Slides)
    • Chapter 3: 3.4, 3.6, 3.8
    • Chapter 4: 4.4
  • Lecture 5: Kernel Smoothing Methods. (Download Slides)
    • Chapter 6: 6.1–6.4, 6.6, 6.8
  • Lecture 6: Basis Expansion, Regularization. (Download Slides)
    • Chapter 5: 5.1, 5.2, 5.4, 5.5, 5.7
    • Recommended Reading Materials: 5.8, 5.9 which are important in functional data analysis and signal processing.
  • Lecture 7: Bagging, Additive Models, Trees. (Download Slides)
    • Chapter 8: 8.7
    • Chapter 9: 9.1, 9.2
  • Lecture 8: Nearest-Neighbor, Random Forests. (Download Slides)
    • Chapter 13: 13.4
    • Chapter 15: 15.1 – 15.4
  • Lecture 9: Boosting. (Download Slides)
    • Chapter 10: 10.1 – 10.5, 10.10, 10.12
  • Lecture 10: Boosting Trees. (Download Slides)
    • Chapter 10: 10.9, 10.11
  • Lecture 11: Introduction to Ensemble Learning, High-Dimensional Problems. (Download Slides)
    • Chapter 16: 16.1 – 16.2
    • Chapter 18: 18.1, 18.7
  • Lecture 12: Neural Networks.
    • Chapter 11

    Other Materials