Course

Credit Type:
Course
ACE ID:
MLS-0111
Version:
1
Organization:
Location:
Online
Length:
24 weeks (168 hours)
Minimum Passing Score:
80
ACE Credit Recommendation Period:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Applied AI and ML
Upper-Division Baccalaureate 3 AI/ML Engineering
Description

Objective:

This course is offered through Coursera, which is an ACE Authorized Instructional Platform.

The course objective is to prepare students for the dynamic field of artificial intelligence and machine learning. Through five courses, students gain a deep understanding of AI and ML fundamentals, practical skills, and hands-on experience.

Starting with the design of scalable AI and ML infrastructure, students learn to build robust environments. They then explore core algorithms and techniques. The program also delves into AI agent development, teaching how to create intelligent systems capable of autonomous troubleshooting using natural language processing (NLP) and decision-making strategies.

A key focus is on leveraging cloud-based AI and ML services, specifically through Microsoft Azure, where students manage end-to-end workflows. The program concludes with advanced concepts, ethical considerations, and a capstone project.

Upon completion, students will have the expertise to design, deploy, and optimize AI and ML solutions, becoming an asset in the tech industry. This program is ideal for those seeking to master AI and ML techniques, build scalable solutions, and apply knowledge to real-world problems.

To be successful, students should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.

Learning Outcomes:

  • Design and Implement AI and ML Infrastructure: Develop environments, including data pipelines, model development frameworks, and deployment platforms
  • Develop Intelligent Troubleshooting Agents: Create AI-powered agents capable of diagnosing and resolving issues autonomously
  • Master AI and ML Algorithms and Techniques: Apply supervised, unsupervised, reinforcement learning, and deep learning methods to solve challenges
  • Leverage Microsoft Azure for AI and ML Workflows: Set up, manage, and optimize the entire AI and ML lifecycle using Azure

General Topics:

  • Drafting a pitch to the C-suite
  • AI/ML applications
  • AI/ML concepts in practice
  • Data management in AI/ML
  • Platform deployment
  • Selecting a framework
  • Producing a comprehensive AI/ML project technical report
  • AI/ML engineering and working with models
  • Deep learning and neural networks
  • Feature selection techniques
  • Reinforcement learning and other approaches
  • Unsupervised learning
  • Drafting the technical report
  • AI agents
  • Fine-tuning LLMs
  • Implementing NLP for troubleshooting
  • Testing and optimizing the agent
  • Troubleshooting agents
  • Data preparation and model training in Azure
  • Model deployment and management in Azure
  • Setting up an AI/ML Azure environment
  • Toward system integration
  • Troubleshooting Azure AI/ML workflows
  • Drafting the technical report (AI graded)
  • End-to-end AI/ML solution design
  • Pragmatic implications
  • AI/ML engineering and advanced techniques
  • Advanced ML techniques
  • Ethical considerations in AI/ML
  • Scalable AI/ML Systems
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Computer Based Training
  • Discussion
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Other
  • Quizzes
  • Peer review graded projects with rubric
Supplemental Materials
Equivalencies