FuzzyFusion_DeepLearning_Tutorial

Fuzzy Fusion of Decisions from Heterogeneous Deep Machine Learning Models

This tutorial will teach participants three key skills that are critical to advancing research in computational intelligence:

1. use of Keras and TensorFlow;
2. deep learning models and transfer learning techniques; and
3. fuzzy machine learning model fusion.

The tutorial session will be broken into these three portions, each of which culminates in code examples that can be immediately migrated from the tutorial to the participants own research thrusts (theories and applications). The session will conclude with a case-study demonstrating how the components have been tied together for scientific studies in remote sensing.

Code Content

Tutorial code content develop by Alex Yang and Bryce Murray Department of Electrical Engineering and Computer Science University of Missouri - Columbia

Please see the Jupyter Notebooks in notebooks

• IntroductionToPyTorch
• PyTorchResNet50
• FuzzyFusion

Docker container image for running tutorial codes

https://hub.docker.com/r/muiidsa/singleuser-pytorch-cpu/

Tutorial Organizer

Grant Scott Department of Electrical Engineering and Computer Science University of Missouri - Columbia & Director, Data Science and Analytics University of Missouri

Sponsors / Support

The tutorial was developed thanks to support of:

• Mizzou High Performance Data-Intensive Computing Systems Laboratory
• Mizzou INformation and Data FUsion Laboratory (MINDFUL)

Deliveries

FUZZ-IEEE Tutorial

• 2019, June 23, 1400 - 1600
• IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),
  • New Orleans, LA, USA
  • June 23-26, 2019
• The tutorial is delivered with support of the University of Missouri's Data Science and Analytics Program