syllabus.Rmd

---
title: "EDS 232: Machine Learning in Environmental Science"
#bibliography: ["ml-env.bib"]
output:
  distill::distill_article:
    pandoc_args: null
---

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

> "Everything that civilisation has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools that AI may provide, but the eradication of war, disease, and poverty would be high on anyone's list. Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." -- Stephen Hawking.

# Course Schedule

-   **Lectures/Labs**: MW 9:30am - 10:45am on Zoom
-   The Zoom link is shared in your Google Calendar invite and on the \#eds-232 Slack Channel for MEDS. It's not share publicly to avoid [Zoombombing](https://en.wikipedia.org/wiki/Zoombombing)

```{r schedule}
source(here::here("_functions.R"))
get_sched() %>% 
  dt_sched()
```

\* NOTE: Reading PDFs are in a Google Drive folder requiring a UCSB Google login for access to prevent copyright infringement. The full reference with source DOI link are listed below under [Readings](#readings).

# Course Description

Machine learning enables predictions from big, complex data into actionable insights. It forms one of the foundations in data science. This course provides a broad introduction to machine learning and statistical pattern recognition with environmental data. Supervised learning (logistic regression, decision trees and neural networks) and unsupervised learning (clustering and ordination) techniques will be applied to the environmental tasks of mapping species distributions and ecological community analyses, respectively. Integer linear programming will be used to optimize biodiversity in planning a reserve. Deep learning techniques (convolutional neural networks) will enable species classification from photos and landcover classification from satellite imagery using Google Earth Engine. The first half of the course will use R and Rmarkdown on the desktop and second half will use Python and Jupyter notebooks in the cloud. This class teaches a broad sweep of advanced scientific programming skills for environmental problem solving.

# Instructor

Ben Best (ben\@ecoquants.com)

-   **Office hours**: Tuesdays TBD on Zoom

# Grading

The majority of points (100 total) will come from the labs. The exams will draw questions from lecture, lab and reading materials. Participation will be based on class attendance.

| Type  | Item                    | Due         | Points |
|-------|-------------------------|-------------|--------|
| Lab   | 1\. Species             | Wed, Jan 19 | 20     |
| Lab   | 2\. Communities         | Wed, Jan 26 | 10     |
| Lab   | 3\. Reserves            | Wed, Feb 2  | 10     |
| Exam  | Mid-Term                | Wed, Feb 2  | 10     |
| Lab   | 4\. iNaturalist         | Wed, Feb 23 | 20     |
| Lab   | 5\. Google Earth Engine | Wed, Mar 9  | 15     |
| Exam  | Final                   | TBD         | 10     |
| Other | Participation           |             | 5      |

# Readings {#readings}

```{r references, results = "asis", echo = FALSE}
librarian::shelf(RefManageR)
bib <- ReadBib("ml-env.bib", check = FALSE)
BibOptions(check.entries = FALSE, style = "markdown", cite.style = "authoryear", bib.style = "authoryear")

NoCite(bib, "zhongMachineLearningNew2021")
NoCite(bib, "elithSpeciesDistributionModels2009")

PrintBibliography(bib, .opts = list(check.entries = FALSE, sorting = "ynt"))
```