• Online, Self-Paced
Course Description

Explore the concepts of expert system along with its Implementation using Java based frameworks, and examine the implementation and usages of ND4J and Arbiter to facilitate optimization.

Learning Objectives

Developing AI and ML Solutions with Java: Expert Systems and Reinforcement Learning

  • list the tools, shells, and programming languages that are being used for Expert Systems
  • work with Jess to create rule based expert systems
  • describe how to define rules and work with expert system shell using Java
  • recognize data notations from the perspective of quality, descriptive, and visualization notations
  • list the different types of datasets and their utility over the various phases of supervised learning
  • identify the various types of Outliers and their impact on the accuracy of the models
  • describe the various approaches of feature relevance search and the evaluation techniques
  • implement principal component analysis data transformation using Java pca-tranform
  • recognize the clustering implementation algorithms and illustrate the validation and evaluation techniques
  • implement hierarchical clustering using the top down approach with Java
  • describe the concept of graph modelling and the various approaches of implementing graphs in machine learning
  • demonstrate how to use datasets with clustering

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.