Mathematical Models¶
This weeks resources focus on providing more detailed information about the mathematics behind many models we have encountered to this point. The materials are a mixture of readings and jupyter notebooks, and are broken into standard conceptual blocks.
Supervised Learning¶
In supervised learning, we know the labels of some data that we would like to predict. Both classification and regression are examples of supervised learning problems.
- **Introduction to Supervised Learning**: Chapter from Introduction to Statistical Learning discussing big picture of regression and classification problems with accompanying R code.
- **Overview of Supervised Learning**: Chapter from Elements of Statistical Learning (ESL) on big idea of supervised learning.
Linear Regression¶
- **PYDSHB: Linear Regression in Depth**: Chapter from Python Data Science Handbook with accompanying Python code.
- **ISLR: Linear Regression Overview**: Introductory level description of Linear Regression from the ISLR textbook. Includes relevant R code.
- **ELEMENTS: Linear Regression Deep**: Rigorous mathematical description of linear regression from the Elements of Statistical Learning.
- **Notes on Regression**: Notes from Andrew Ng’s class introducing Linear Regression.
Classification¶
- **PYDSHB: Naive Bayes in Depth**: Overview of classification using Bayes Theorem from the Python Data Science Handbook. Jupyter notebook with accompanying Python code.
- **ISLR: Classification**: Introductory mathematical and statistical presentation of classification using Logistic Regression from ISLR textbook. Contains relevant R code.
- **ESL: Classification**: Rigorous development of classification from Elements of Statistical Learning.
- **Notes on Classification**: Notes from Andrew Ng’s class introducing classification and Logistic Regression.
Unsupervised Learning¶
- **PYDSHB**: KMeans Clustering: KMeans from Python Data Science Handbook including Python code and examples.
- **PYDSHB**: Gaussian Mixture Models: Gaussian Mixture Models in Python and examples from the Python Data Science Handbook.
- **ISLR: Unsupervised Learning**: Chapter from Introduction to Statistical Learning introducing unsupervised learning with accompanying R code.
- **ESL: Unsupervised Learning**: Rigorous introduction to unsupervised learning from the Elements of Statistical Learning.