
Handson Supervised Learning: Part 2  Classification and Random Forests (2nd offering)
30 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...

Handson Sequential Learning and Design of Experiments
29 Apr 2020   Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial introduces the concept of sequential learning and information acquisition functions and how these algorithms can help reduce the number of experiments required to find an optimal candidate. A handson approach is presented to optimize the ionic conductivity of ceramic...

Handson Supervised Learning: Part 2  Classification and Random Forests (1st offering)
24 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces neural networks for classification tasks and random forests for regression tasks via Jupyter notebooks on nanoHUB.org. You will learn how to create and train a neural network to perform a classification, as well as how to define and train random forests. The tools used...

Handson Supervised Learning: Part 1  Linear Regression and Neural Networks
22 Apr 2020   Contributor(s):: Saaketh Desai
This tutorial introduces supervised learning via Jupyter notebooks on nanoHUB.org. You will learn how to setup a basic linear regression in a Jupyter notebook and then create and train a neural network. The tool used in this demonstration is Machine Learning for Materials Science:...

Handson Data Science and Machine Learning Training
21 Apr 2020   Contributor(s):: Alejandro Strachan, Saaketh Desai
This series of handson tutorials is designed to jump start your use of data science and machine learning in research or teaching. This series will cover the following topics:Learn how to use Jupyter notebooks for your researchInteract with data repositories and manage...

Introduction to Jupyter Notebooks, Data Organization and Plotting (1st offering)
21 Apr 2020   Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...

Introduction to Jupyter Notebooks, Data Organization and Plotting (2nd offering)
21 Apr 2020   Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
This tutorial gives an introductory demonstration of how to create and use Jupyter notebooks. It showcases the libraries Pandas to manipulate and organize data with functionalities similar to those of Excel on python, and Plotly, a library used to create interactive plots for enhanced...

Machine Learning Workshop for Materials Science
27 Jan 2020   Contributor(s):: Saaketh Desai
This workshop covers the fundamentals of machine learning and data science, with a focus on material science applications. This workshop includes a handson demonstration of the nanoHUB tool Machine Learning for Materials Science: Part 1.

ECE 595ML: Machine Learning I
17 Jan 2020   Contributor(s):: Stanley H. Chan
Spring 2020  This course is in productionCourse Website: https://engineering.purdue.edu/ChanGroup/ECE595/index.htmlCourse Outline:Part 1: Mathematical BackgroundLinear Regression and OptimizationPart 2: ClassificationMethods to train linear classifiersFeature analysis, Geometry, Bayesian...

Citrine Tools for Materials Informatics
05 Dec 2019   Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
Jupyter notebooks for sequential learning in the context of materials design. Run your own models, explore various methods and adapt the notebooks to your needs.

Machine Learning for Materials Science: Part 1
09 Feb 2019   Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan, Saaketh Desai
Machine learning and data science tools applied to materials science

TensorFlow Tutorials
03 Dec 2018   Contributor(s):: Juan Carlos Verduzco Gastelum, Saaketh Desai, Alejandro Strachan
Readytorun Jupyter notebooks for machine learning using Tensorflow and Keras