Implementing AI Using Cognitive Modeling


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Cognitive modeling can provide additional human qualities to AI systems. It is traditionally used in cognitive machines and expert systems. However, with extra computing power, it can be applied to more profound AI approaches like neural networks and reinforcement learning systems. Knowledge of cognitive modeling applications is essential to any AI developer aspiring to design AI architectures and develop large-scale applications.

In this course, you'll examine the role of cognitive modeling in AI development and its possible applications in NLP, image recognition, and neural networks. You'll outline core cognitive modeling concepts and significant industry use cases. You'll list open source cognitive modeling frameworks and explore cognitive machines, expert systems, and reinforcement learning in cognitive modeling. Finally, you'll use cognitive models to solve real-world problems.



Expected Duration (hours)
0.8

Lesson Objectives

Implementing AI Using Cognitive Modeling

  • discover the key concepts covered in this course
  • describe the concept of cognitive modeling and its importance
  • specify how cognitive modeling can be used in AI applications
  • list companies that implement cognitive modeling approaches
  • describe how to use cognitive modeling for natural language processing
  • outline how to use cognitive modeling for image recognition
  • identify how to use cognitive modeling with neural networks
  • list open source cognitive modeling frameworks and describe their uses
  • name some real-life use cases of cognitive modeling
  • define expert systems in terms of cognitive modeling
  • describe cognitive machines and specify their use and importance
  • describe the role of reinforcement learning in cognitive modeling
  • determine ways in which AI can reason in a similar way to the human brain
  • describe memory-augmented neural network architecture for learning complex tasks
  • work with natural language processing techniques to develop a smart question answering (QA) system
  • work with an AI system that can observe, reason, and predict tags for YouTube videos
  • create an AI algorithm that can predict heart disease using MRI images
  • summarize the key concepts covered in this course
  • Course Number:
    it_aispcgdj_01_enus

    Expertise Level
    Expert