Online Presentations (1-6 of 6)

1. MatSci 395 Lecture 1: Introduction to Machine Learning, Materials Imaging, and Segmentation

   Online Presentations | 29 Sep 2021 | Contributor(s):: Tiberiu Stan

2. Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene

   Online Presentations | 27 Jan 2021 | Contributor(s):: Aagam Rajeev Shah

   This tutorial will introduce you to some basic image segmentation techniques driven by unsupervised machine learning techniques such as the Gaussian mixture model and k-means clustering. You will learn how to implement k-means clustering and template matching, and use these to segment a...

3. Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (2nd offering)

   Online Presentations | 30 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan

   This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...

4. Hands-on Unsupervised Learning using Dimensionality Reduction via Matrix Decomposition (1st offering)

   Online Presentations | 29 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan

   This tutorial introduces unsupervised machine learning algorithms through dimensionality reduction via matrix decomposition techniques in the context of chemical decomposition of reactive materials in a Jupyter notebook on nanoHUB.org. The tool used in this demonstration...

5. [Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations

   Online Presentations | 29 Oct 2013 | Contributor(s):: Thomas J. Anastasio

   Unsupervised learning algorithms, given only a set of input patterns, can train neural networks to form distributed representations of those patterns that resemble brain maps.

6. [Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning

   Online Presentations | 29 Oct 2013 | Contributor(s):: Thomas J. Anastasio

   Unsupervised learning algorithms can train neural networks to increase the amount of information they contain about their inputs and simulate the properties of sensory neurons.