01.Rmd

# Introduction

**Learning objectives:**

- Recognize various types of statistical learning.
- Understand why this book is useful for you.
- Be able to read mathematical notation used throughout this book.
- Describe the overall layout of this book.
- Be able to find data used in examples throughout the book.

## What is statistical learning?

- More about this in Chapter 2.
- **Supervised:** "Building a model to **predict an *output* from *inputs*.**"
  - Predict `wage` from `age`, `education`, and `year`.
  - Predict `market direction` from `previous days' performance`.
- **Unsupervised:** Inputs but no specific outputs, **find relationships and structure.**
  - Identify `clusters` within `cancer cell lines`.

## Why ISLR?

- "Facilitate the transition of statistical learning from an academic to a mainstream field."
- Machine learning* is useful to everyone, let's all learn enough to use it responsibly.
- R "labs" make this make sense for this community!

## Notation

- *n* = number of observations (rows)
- *p* = number of features/variables (columns)
- We'll come back here if we need to as we go!
- Some symbols they assume we know:
  - $\in$ = "is an element of", "in"
  - ${\rm I\!R}$ = "real numbers"

## What have we gotten ourselves into?

- 2: Terminology & main concepts
- 3-4: Classic linear methods
- 5: Resampling (so we can choose the best method)
- 6: Modern updates to linear methods
- 7+: Beyond Linearity (we can worry about details as we get there)

## Where's the data?

```{r 01-install, eval = FALSE}
install.packages("ISLR2")
```

Or "install" this book.

```{r 01-install-book, eval = FALSE}
install.packages("remotes")
remotes::install_github("r4ds/bookclub-islr")
remove.packages("bookclubislr") # This isn't really a package.
```

## Meeting Videos

### Cohort 1

`r knitr::include_url("https://www.youtube.com/embed/URL")`

<details>
<summary> Meeting chat log </summary>

```
ADD LOG HERE
```
</details>