index.Rmd

---
title: "EDS 232: Machine Learning in Environmental Science"
description: ""
site: distill::distill_website
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)

# Learn more about creating websites with Distill at:
# https://rstudio.github.io/distill/website.html

# Learn more about publishing to GitHub Pages at:
# https://rstudio.github.io/distill/publish_website.html#github-pages

```

```{r, out.width = "100%", fig.cap = "Image created using the Midjourney image generation tool"}

# UPDATE IMAGE HERE 
# or copy/paste this code elsewhere, updating the file path, to add other images to your site!
knitr::include_graphics("img/MLRobotLearnBanner.png")
```

## Welcome to the EDS 232 website

## Course description

Machine learning is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. In this course, we focus on the core concepts of machine learning that beginning ML researchers must know. We cover 'classical machine learning' primarily using R, and explore applications to environmental science. To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable.

## Teaching team

**Instructor:** Mateo Robbins (mjrobbins\@ucsb.edu)

-   **Office:** NCEAS Group Office
-   **Office hours:** Monday 11:00-12:00

**Teaching assistant:** Brian Lee (brianlee52\@bren.ucsb.edu)

-   **Office:** TBD
-   **Office hours:** TBD

## Important links

-   [Link to full course syllabus](https://docs.google.com/document/d/1VYZQ6cZq4jL8bdFtronp3Qf1BgTOBpXD2BIG5OSK7eM/edit?usp=sharing)

## Weekly course schedule

-   **Lectures:** M, W 9:30 - 10:45am PST (NCEAS)

## Learning objectives

The goal of EDS 232 is to equip students with a strong foundation in the core concepts of machine learning. By the end of the course, students should be able to: 

-   Build machine learning models in R using popular machine learning packages

-   Build and train supervised machine learning models for prediction and binary classification tasks, including linear and logistic regression.

-   Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

-   Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

-   Use unsupervised learning techniques for unsupervised learning: including clustering.

## Course requirements

### Computing

-   [**Minimum MEDS device requirements**](https://ucsb-meds.github.io/computer_reqs.html)

-   `R` version 4.0.2 (or higher)

-   RStudio version 1.4.1103 (or higher)

### Textbook

[*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/), by Bradley Boehmke and Brandon Greenwell

### 

## Tentative topics

| Week \# | Dates            | Lecture                                                        |
|---------|------------------|----------------------------------------------------------------|
| 1       | 1/9, 1/11        | Introduction, Linear Regression and ML Modeling Fundamentals I |
| 2       | MLK, 1/18        | Linear Regression and ML Modeling Fundamentals II              |
| 3       | 1/23, 1/25       | Regularized Regression                                         |
| 4       | 1/30, 2/1        | Logistic Regression, Classification                            |
| 5       | 2/6, 2/8         | Random Forests, Decision Trees                                 |
| 6       | 2/13, 2/15       | Gradient Boosting                                              |
| 7       | Presidents, 2/22 | Ethics, Justice, Bias                                          |
| 8       | 2/27, 3/1        | K-Means and Hierarchical Clustering                            |
| 9       | 3/6, 3/8         | Deep Learning and Support Vectors                              |
| 10      | 3/13, 3/15       | TBD                                                            |