Developing AI and ML Solutions with Java: Expert Systems and Reinforcement Learning Overview/Description Expected Duration Lesson Objectives Course Number Expertise Level Overview/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.
Expected Duration (hours)
0.9
Lesson 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
Course Number: it_sdjaai_05_enus
Expertise Level
Intermediate