BIA682 Deep Learning: Special Types of Neural Networks and Applications (3 cr.)

Prerequisite(s): BIA681  
This course continues the exploration of deep neural networks by building special types of neural networks for various applications. Working with Python Keras, students build a neural network model and evaluate its results. Students explore different neural network architectures, such as recurrent neural networks and compare them to basic neural networks.

Upon completion of this course, students are expected to be able to do the following:

1. Demonstrate the application of neural networks for time series/sequence data.
2. Evaluate and compare the usage of neural networks with traditional methods for time series/sequence data.
3. Construct prediction models using special types of neural networks with the Keras libraries.
4. Assess deep learning model results to improve accuracy.
5. Communicate neural network results to stakeholders.
6. Explain other deep learning techniques and applications.