Intro#
Overview#
Today’s materials will provide an overview of data science and machine learning and how these topics can be applied to topics related to climate science and climate change. Particularly, you will explore a Earth System Model based data set that contains temperature, precipitation, and anthropogenic emission variables over a time span of half a century. Furthermore, you will learn how to set up machine learning models that can predict output values and categorize data.
Day Learning Objectives#
See how machine learning and AI can be used to address many different problems associated with climate change.
Understand the basic principles of machine learning.
Apply a basic machine learning technique to the problem of rising temperatures.
Identify the pros and cons of applying machine learning methods in this domain.
Install and import feedback gadget#
Show code cell source
# @title Install and import feedback gadget
!pip3 install vibecheck datatops --quiet
from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
return DatatopsContentReviewContainer(
"", # No text prompt
notebook_section,
{
"url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
"name": "comptools_4clim",
"user_key": "l5jpxuee",
},
).render()
feedback_prefix = "W2D4_Intro"
[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: pip install --upgrade pip