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Hands-on Learning Modules on Data Science and Machine Learning in Engineering

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Join Policy: Open/Anyone
Created: 29 Sep 2020
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Overview

  1. Active learning
  2. data science
  3. machine learning
  4. materials discovery
  5. materials science
  6. NCN Group - Machine Learning Modules

This series of modules introduces key concepts in data science in the context of application in materials science and engineering. The end to end modules include:

 

The modules are self-contained and modular, they are designed for easy incorporation into existing courses or for those interested in self-study.

All interactive computing is performed using cloud computing in nanoHUB, there is no need to download or install any software. All resources are open and free.

 

Knowledge and Skills

 
DATA HANDLING
 
Data collection, completeness, and provenance:
 
 
Data storage and sharing:
 
 
Data querying, organization, and filtering:
 
PREDICTIVE MODELING
Data visualization:
Digital representation and descriptors for materials:
 
Simple regression models:
 
 
Machine learning models for regression and classification:
 
 
Random forests and decision trees:
 
DECISION MAKING
 
Uncertainty quantification
(coming soon):
Coming Soon
 
 
 
 
Active learning for design of experiments:

 

Pre-requisites

The interactive computing is performed using Python through Jupyter notebooks. Basic programming skills are required. An introductory tutorial on Jupyter, Python and Plotting is available.

 

Module list