Course Summary
Credit Type:
31.5 hours and 31 lab hours
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 artificial intelligence


The course objective is to provide learners with the knowledge and skills required to be an AI Apprentice and to build upon those skills to eventually become an AI Architect. The course starts by providing machine learning (ML) developers with knowledge on artificial intelligence (AI) and its fundamental aspects, including history, recent database developments, and the primary "approaches" to AI. From there, the focus is on becoming an AI developer with working knowledge of developing AI solutions using a variety of tools and frameworks that are essential to AI development. Next, they will move on to AI practitioner topics, where they will learn to optimize and tune AI solutions to deliver the best possible performance. Then finally, learners can become AI Architects by bringing together all the insights and knowledge they have acquired. They will graduate to implementing advanced AI enterprise planning and creating reusable AI architecture patterns. Learner will also explore current and future AI technologies, frameworks, and Explainable Artificial Intelligence (XAI).

Learning Outcomes:

  • recognize the different AI frameworks and identify key features and use cases
  • work with Google BERT, the Keras Framework, and Microsoft Cognitive Toolkit (CNTK)
  • use Apache Spark for AI Development
  • use Amazon ML with AWS to work with big data, and to create machine and deep learning models
  • explore advanced CNTK, Keras, Apache Spark, Amazon Machine Learning, and how to build intelligent information systems
  • differentiate between architecture and design patterns and how they're used
  • compare the advantages and disadvantages of common AI platforms and frameworks
  • define Explainable Artificial Intelligence (XAI), how it is used, and the data structures behind XAI's preferred algorithms
  • identify which AI technologies are common across all industries and which are industry-specific.
  • explore AI development and theory, HCI principles and methods, AI development with Python, computer vision for AI and cognitive modelling

General Topics:

  • AI Development Theory
  • HCI principles and methods
  • Python AI development
  • Computer vision for AI
  • Cognitive models
  • AI framework overview
  • Microsoft Cognitive Toolkit (CNTK) - an introduction
  • Keras - an introduction
  • Apache Spark - an introduction
  • Amazon Machine Learning - an introduction
  • Applying cognitive models
  • AI and robotics
  • Working with Google BERT - an introduction
  • The AI practitioner role
  • Extending Microsoft CNTK
  • Extending Keras
  • Extending Apache Spark
  • Extending Amazon ML
  • Building intelligent information systems
  • Google BERT best practices and examples
  • Elements of an artificial intelligence architect
  • AI enterprise planning
  • AI in industry
  • Reusable AI architecture patterns
  • Current and future AI technologies and frameworks
  • Explainable Artificial Intelligence (XAI)
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Quizzes

Minimum Passing Score:

Supplemental Materials