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Hands-on Sequential Learning and Design of Experiments
Online Presentations | 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 hands-on approach is presented to optimize the ionic conductivity of ceramic...
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Image Segmentation for Graphene Images
Online Presentations | 29 Apr 2020 | Contributor(s):: Joshua A Schiller
This lecture outlines the need for a fast, automated means for identifying regions of images corresponding to graphene. Simple methods, like color masking and template matching, are discussed initially. Unsupervised clustering methods are then introduced as potential improvements...
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Hands-on Supervised Learning: Part 2 - Classification and Random Forests (1st offering)
Online Presentations | 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...
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Hands-on Supervised Learning: Part 1 - Linear Regression and Neural Networks
Online Presentations | 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:...
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Introduction to Jupyter Notebooks, Data Organization and Plotting (1st offering)
Online Presentations | 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...
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Introduction to Jupyter Notebooks, Data Organization and Plotting (2nd offering)
Online Presentations | 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...
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Toward a Thinking Microscope: Deep Learning-Enabled Computational Microscopy and Sensing
Online Presentations | 29 Jan 2020 | Contributor(s):: Aydogan Ozcan
In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.
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MSEML: Machine Learning for Materials Science Tool on nanoHUB
Online Presentations | 27 Jan 2020 | Contributor(s):: Saaketh Desai
This talk is a hands-on demonstration using the nanoHUB tool Machine Learning for Materials Science: Part 1.
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Data Science and Machine Learning for Materials Science
Online Presentations | 22 Jan 2020 | Contributor(s):: Saaketh Desai
This talk covers the fundamentals of machine learning and data science, focusing on material science applications. The talk is for a general audience, attempting to introduce basic concepts such as linear regression, supervised learning with neural networks including forward and back...
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ECE 595ML Lecture 1.1: Linear Regression
Online Presentations | 21 Jan 2020 | Contributor(s):: Stanley H. Chan
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ECE 595ML Lecture 2.1: Regularized Linear Regression
Online Presentations | 21 Jan 2020 | Contributor(s):: Stanley H. Chan
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ECE 595ML: Introduction
Online Presentations | 17 Jan 2020 | Contributor(s):: Stanley H. Chan
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Entanglement, Inc - revolutionizing computation | transforming ai
Online Presentations | 06 Nov 2019 | Contributor(s):: Jason Turner
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Universal Variational Quantum Computation
Online Presentations | 28 Oct 2019 | Contributor(s):: Jacob Biamonte
We show that the variational approach to quantum enhanced algorithms admits a universal model of quantum computation.
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Machine Learning for Quantum Control
Online Presentations | 28 Oct 2019 | Contributor(s):: Barry Sanders
We develop a framework that connects reinforcement learning with classical and quantum control, and this framework yields adaptive quantum-control policies that beat the standard quantum limit, inspires new methods for improving quantum-gate design for quantum computing, and suggest new ways to...
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Perspectives on High-Performance Computing in a Big Data World: Part D - Learning Model Details and Agent-Based Simulations
Online Presentations | 17 Oct 2019 | Contributor(s):: Fox, Geoffrey C.
This lecture completes the discussion of MLforHPC. It covers Learning Model Details and Agents and Time-Series Case Studies.
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Perspectives on High-Performance Computing in a Big Data World: Part E - Challenges and Opportunities, Conclusions
Online Presentations | 17 Oct 2019 | Contributor(s):: Fox, Geoffrey C.
This lecture covers the computer science issues raised in this talk. The conclusions note that HPDC/HPC is essential; it is good to work closely with industry with student Internships and Collaborations; the Global AI and Modeling Supercomputer GAIMSC is a good framework with an HPC Cloud linked...
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Perspectives on High-Performance Computing in a Big Data World: Part C - MLaroundHPDC/HPC and MLAutotuning
Online Presentations | 10 Oct 2019 | Contributor(s):: Fox, Geoffrey C.
This is the first part of the discussion of MLforHPC. It includes MLAutotuning (Using ML to configure or autotune ML or HPC simulations and MLaroundHPC (Learning outputs from inputs).
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Perspectives on High-Performance Computing in a Big Data World: Part B - More on the Evolution of Interests and Communities
Online Presentations | 30 Sep 2019 | Contributor(s):: Fox, Geoffrey C.
This part contains several topics. It discusses the importance of industry in several facets of the field: SysML conference, clouds, MLPerf, the Global AI Supercomputer. The nature of data science and data engineering jobs. We emphasize the need for HPC. We finish by introducing MLforHPC (AI for...
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3 min Research Talk: Web-based Machine Learning Tool for Material Discovery and Property Prediction
Online Presentations | 26 Sep 2019 | Contributor(s):: Bryan Arciniega
This model allows the end-user to increase their knowledge on a scarce data set by using a data-rich property set. We also investigate the effect of chemical representation and autoencoder type on property prediction and compound generation.