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review_midterm.Rmd
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---
title: "Mid-Term Review"
author: "Ben Best"
date: "1/31/2022"
output: html_document
bibliography: ["ml-env.bib"]
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Species: Supervised Learning
## Introduction
- Lecture: [Introduction](https://docs.google.com/presentation/d/1ZNIH-DdHy2B-gJhbVmrMPPiICP_1Q0B0YfqaDmz-mRw/edit?usp=sharing) <a href='https://youtu.be/KmQzGVx6lO8' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- What is **Machine Learning**?
> "**Machine learning** (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data..." - [Wikipedia](https://en.wikipedia.org/wiki/Machine_learning)
- **Supervised Learning** vs **Unsupervised Learning** vs **Semi-Supervised Learning** vs **Reinforcement Learning**
- ML **workflow**: **split** into ***training*** and ***test*** data, **fit** ***model*** on ***training*** data, **predict** on ***new*** data, **evaluate** model with ***test*** data
- **Accuracy** vs **precision**
- **Artificial Intelligence** (AI) \> **Machine Learning** (ML) \> **Deep Learning** (DL)
- [History of machine learning \| Google Cloud](https://cloud.withgoogle.com/build/data-analytics/explore-history-machine-learning/)
- 1950: Alan Turing comes up with [**Turing test**](https://en.wikipedia.org/wiki/Turing_test#:~:text=The%20Turing%20test%2C%20originally%20called,from%2C%20that%20of%20a%20human.&text=If%20the%20evaluator%20cannot%20reliably,to%20have%20passed%20the%20test.): originally called the "imitation game" (see also [Imitation Game](https://www.google.com/search?q=imitatio+game+movie&rlz=1C5CHFA_enUS833US833&oq=imitatio+game+movie&aqs=chrome..69i57j46i13j0i13l7.5092j0j9&sourceid=chrome&ie=UTF-8) movie with Benedict Cumberbatch on cracking German codes to save WWII), which is the test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. He asked "Can machines think?", in his 1950 seminal paper "[Computing Machinery and Intelligence](https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence "Computing Machinery and Intelligence")".
- 2015: Google's **AlphaGo** was the first program to best a professional player at Go, considered the most difficult board game in the world. With this defeat, computers officially beat human opponents in every classical board game.
- Ecology and Environment applications:
- individual \> population \> landscape \> ecosystem \> global
- Deep Learning architecture of **convolutional neural nets** (CNNs in next module): sounds, images
- **Species Distribution Modeling**
- Elith & Leathwick (2009): geographic -\> environmental (fit) -\> geographic (predict)
- modeling techniques: GLM, GAM, TREE (rpart, RandomForest)
- **Ecological Niche**
- Grinell: environmental conditions
- Elton: env + biotic
- Hutchinson: "n-dimensional hypervolume"
- **fundamental** vs **realized** niche
- Lab: [Species: explore](https://bbest.github.io/eds232-ml/lab1a_sdm-explore.html)
- Query the Global Biodiversity Information Facility ([GBIF.org](https://www.gbif.org/)) for observations
- Fetch environmental data to be used as predictors
- Reading: [Zhong et al. (2021)](https://drive.google.com/open?id=1-LYnsLS7ze68f3CNp6I-kFxuGPceGk5c&authuser=benbest%40ucsb.edu&usp=drive_fs) Machine Learning: New Ideas and Tools in Environmental Science and Engineering
- inputs: tabular, image, graph, text
- applications: making predictions; extracting feature importance; detecting anomalies; and discovering new materials/chemicals
- workflow: preparation, model development, interpretation and deployment
## Logistic Regression
- Lecture: [Logistic Regression](https://docs.google.com/presentation/d/1Oh6lgAAuoktI-M8pjDYSRp0FqTh3JANQhczeR4uVHYw/edit?usp=sharing) <a href='https://youtu.be/5z5dcmHg1tY' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- Lab: [Species: regress](https://bbest.github.io/eds232-ml/lab1b_sdm-regress.html)
- Reading: [Elith & Leathwick (2009)](https://drive.google.com/open?id=1-LPyMekhOQPkwOJzDYj6RQf7S5wlH8wn&authuser=benbest%40ucsb.edu&usp=drive_fs) Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
- <div>
> Key steps: gathering relevant data; assessing its adequacy (the accuracy and comprehensiveness of the species data; the relevance and completeness of the predictors); deciding how to deal with correlated predictor variables; selecting an appropriate modeling algorithm; fitting the model to the training data; evaluating the model including the realism of fitted response functions, the model's fit to data, characteristics of residuals, and predictive performance on test data; mapping predictions to geographic space; selecting a threshold if continuous predictions need reduction to a binary map; and iterating the process to improve the model in light of knowledge gained throughout the process.
</div>
## Decision Trees
- Lecture: [Decision Trees](https://docs.google.com/presentation/d/1R4y-QXcIAcRDpmU9_-blpU4_Jkd7olwzVe3BU2w0lF0/edit?usp=sharing) <a href='https://youtu.be/8BsNybYh0Ls' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- Lab: [Species: trees](https://bbest.github.io/eds232-ml/lab1c_sdm-trees.html)
- Reading: [Evans et al. (2011)](https://drive.google.com/file/d/1FYzbDjCxwacR0iZZmmDJ0Albcw00jru2/view?usp=sharing) Modeling Species Distribution and Change Using Random Forest
- issues: complex non-linear interactions, spatial autocorrelation, high-dimensionality, non-stationary, historic signal, anisotropy, and scale
- Classification and Regression Trees versus Random Forest Algorithm (ensemble)
- Model Selection (parsimony), Imbalanced Data, Model Validation
- Current and Future Prediction
## Model Evaluation
- Lecture: [Model Evaluation](https://docs.google.com/presentation/d/1wPfEEhhSdtA6q7wXpucrxop3dTgJrzYdyFskDQAbq3E/edit?usp=sharing) <a href='https://youtu.be/taAM6Bo8bDU' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- Lab: [Species: evaluate](https://bbest.github.io/eds232-ml/lab1d_sdm-evaluate.html)
- Reading: [Andrade et al. (2020)](https://drive.google.com/open?id=16SIJiRTII6Lj5Ht0xd23DlYacVi477RJ&authuser=ben%40ecoquants.com&usp=drive_fs) ENMTML: An R package for a straightforward construction of complex ecological niche models
- Overview of issues and methodologies
# Communities: Unsupervised Learning
## Clustering
- Lecture: [Biodiversity; Clustering](https://docs.google.com/presentation/d/1TdIjzc5Lu1WCNMMoxyddnt_GO8jLNS_Vc7cCgrxG3lA/edit?usp=sharing) <a href='https://youtu.be/4zXXjgEqPA0' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- Lab: [Communities: cluster](https://bbest.github.io/eds232-ml/lab2a_community-cluster.html)
- Reading: [Ch. 8-9 (p.123-152) of Kindt & Coe (2005)](https://drive.google.com/open?id=16T7kAO9W5Rz7xSHqwJrOdDu8RUTmTJ89&authuser=ben%40ecoquants.com&usp=drive_fs)\
Ch. 8 Analysis of differences in species composition; Ch. 9 Analysis of ecological distance by clustering
- **Ecological Distance** metrics to build matrices of between site dissimilarity based on species composition: **Euclidean**, **Manhattan**, **Bray-Curtis**
- **Hierarchical clustering** in ecological context
- **Dendrograms**
- Complements **ordination**
## Ordination
- Lecture: [Ordination](https://docs.google.com/presentation/d/1xsRkZq-5U2MakxHTujMPhTJWOeGe7Bs1RGzHM_XPlU0/edit?usp=sharing) <a href='https://youtu.be/p_nZbI9taIc' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- Lab: [Communities: ordinate](https://bbest.github.io/eds232-ml/lab2b_community-ordination.html)
# Reserves: Optimization
## Conservation Planning
- Lecture: [Conservation Planning](https://docs.google.com/presentation/d/13BmRTMSv_XFeiwJYymbfrQ0ZF5xhI6Yr_OWqD4MXn08/edit?usp=sharing) <a href='https://youtu.be/ZAc7TaCDlMY' target='_blank'><i class='fab fa-youtube' role='presentation' aria-label='youtube icon'></i></a>
- Lab: [Reserves](https://bbest.github.io/eds232-ml/lab3_reserves.html)