Applied Statistics Table of Contents

BACK: Classes

UNDER HEAVY CONSTRUCTION: NOT EVERYTHING MAY BE CORRECT.

Preface

I am currently taking this class (Fall 2018) and kind of using this page as my notes.

I will be taking things out of our textbook, which can be found here. It includes datasets that will be played around with, using R. I’ll also be taking things from Professor Sindi’s notes.

I don’t think I need to talk about how important data science is nowadays–it’s all the hype! This class (I hope) is going to teach ways to analyze data for the sake of prediction, inference, or to generally extract information from a dataset.

I also plan to implement some of the algorithms that we learn in Python to practice my awful Python and numpy (and eventually some web app with React). You can find that here.

Some notation:

$$\begin{bmatrix}a_{11}&...&a_{1p}\\...&&\\a_{n1}&...&a_{np}\end{bmatrix}$$ $$\begin{bmatrix}X_1&X_2&...&X_p \end{bmatrix}, \textrm{where } X_n \textrm{ is a column vector representing a feature.}$$

…

  1. Statistical Learning
  2. Linear Regression
  3. Classification
  4. Resampling Methods
  5. Improving Linear Models
  6. Trees, Random Forests
  7. Supper Vector Machines
  8. Unsupervised Learning