Course

Credit Type:
Course
ACE ID:
UNLV-0008
Version:
1
Organization:
University of Louisville
Location:
Online
Length:
80 hours
Minimum Passing Score:
80
ACE Credit Recommendation Period:
Credit Recommendation & Competencies
Competency Framework Statement
NICE Workforce Framework for Cybersecurity Artificial Intelligence (AI) Security - This Competency Area describes a learner’s capabilities to secure Artificial Intelligence (AI) against cyberattacks, to ensure it is adequately contained where it is used, and to mitigate the threat AI presents where it or its users have malicious intent (NF-COM-002)
Description

Objective:

The course objective is to cover blockchain topics for learners who want to focus on specific topics within cybersecurity.

Learning Outcomes:

  • Coding: Acquire proficiency in coding by understanding syntax, utilizing variables, functions, operators, and control structures, and applying these skills to manipulate data and construct functional programs
  • Artificial Intelligence: Explore the evolution of AI, various learning approaches, data and knowledge representation techniques, and the legal, ethical, and privacy considerations associated with AI applications in cybersecurity and healthcare
  • Cognitive Computing: Explore the integration of computer science with neuroscience to develop systems that mimic human cognitive processes, enhancing adaptability and decision-making capabilities
  • Robotics Process Automation Analysis: Analyze the system architecture enabling robotics, identify common threats and cyber-attacks, and address ethical issues related to robotics in healthcare to ensure secure and trustworthy operations
  • Data Mining: Apply data mining techniques to uncover hidden patterns and insights within large datasets, enhancing decision-making processes and strategic planning
  • Blockchain: Examine the security mechanisms behind blockchain technology, its applications in various sectors, including healthcare, and analyze use cases such as IBM Hyperledger Fabric

General Topics:

  • Coding
  • Artificial Intelligence
  • Cognitive Computing
  • Robotics Process Automation Analysis
  • Data Mining
  • Block Chain
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Computer Based Training
  • Practical Exercises
  • Project-based Instruction
  • Course content materials are presented in an interactive text format, via Articulate Rise presentations.

Methods of Assessment:

  • Case Studies
  • Examinations
  • Quizzes
  • Written Papers
Supplemental Materials
Equivalencies

Other offerings from University of Louisville

View All Courses
College Credit Opportunities