index.Rmd

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

```{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 at NCEAS. For Covid compliance, lectures will be available via Zoom link that is shared in your Google Calendar invite and on the \#eds-232 Slack Channel for MEDS (and not shared publicly to avoid [Zoombombing](https://en.wikipedia.org/wiki/Zoombombing)).

-   [**Feedback Form**](https://forms.gle/GW9SBEXg9fuQvX3A7){target="_blank"}. Please share any/all feedback anonymously and as frequently, e.g. weekly, as you like. Your input is always welcome and appreciated.

-   **Instructor**: Ben Best (ben\@ecoquants.com)

-   **Office hours**: Mondays 11am-12:30 pm on Zoom / NCEAS office

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

\* NOTE: Readings in the Course Schedule reference articles in Author (Year) format and _textbooks_ with acronyms in italics. PDFs are in a Google Drive folder requiring a _**UCSB Google login**_ for access to prevent copyright infringement. The full references 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.

# Grading

The majority of points (100 total) will come from the labs. The Mid-Term exam will draw questions from lecture, lab and reading materials. For the [Group Project](./gp_kaggle.html) you will select a [Kaggle competition](https://www.kaggle.com/competitions), submit an entry, and present on it for the last day of class. Participation will be based on class attendance.

| Type  | Item                      | Points |
|-------|---------------------------|--------|
| Lab   | 1\. Species Distributions | 20     |
| Lab   | 2\. Communities           | 10     |
| Lab   | 3\. Reserves              | 10     |
| Exam  | Mid-Term                  | 10     |
| Lab   | 4\. Species Images        | 15     |
| Lab   | 5\. Satellite Imagery            | 10     |
| GP    | Group Project             | 20     |
| Other | Participation             | 5      |

# Readings

## Textbooks

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

```{r, print_refs, results='asis', echo=FALSE, warning=FALSE, message=FALSE}

bib_txt <- function(acronym, link, bibkey){
  writeLines(glue("- [_{acronym}_]({link}) "))
  print(
    bib[key = bibkey], 
    .opts = list(
      check.entries = FALSE, 
      style = "html", 
      bib.style = "authoryear"))
}
bib_txt(
  "DLR", "https://drive.google.com/open?id=16Tr0IYufMnTqszkyzXzRM0XmQDjWuwXv&authuser=ben%40ecoquants.com&usp=drive_fs",
  "cholletDeepLearning2018")
bib_txt(
  "DLPy", "https://drive.google.com/open?id=16TxVeHT_7ktz6Oh7xcZ3uMzxHaNQ16Dc&authuser=ben%40ecoquants.com&usp=drive_fs",
  "cholletDeepLearningPython2018")
bib_txt(
  "HOMLR", "https://bradleyboehmke.github.io/HOML/",
  "greenwellHandsOnMachineLearning2020")
bib_txt(
  "SDMR", "https://rspatial.org/raster/sdm/",
  "hijmansSpeciesDistributionModeling2021")
bib_txt(
  "TDA", "https://drive.google.com/open?id=16T7kAO9W5Rz7xSHqwJrOdDu8RUTmTJ89&authuser=ben%40ecoquants.com&usp=drive_fs",
  "kindtTreeDiversityAnalysis2005")
```

## Articles

```{r references, results = "asis", echo = FALSE}
NoCite(bib, "zhongMachineLearningNew2021")
NoCite(bib, "elithSpeciesDistributionModels2009")
NoCite(bib, "evansModelingSpeciesDistribution2011")
NoCite(bib, "andradeENMTMLPackageStraightforward2020")
NoCite(bib, "oksanenMultivariateAnalysisEcological2022")

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