Revised 7/2025

ITD 195 - Artificial Intelligence (AI) Practitioner (3 CR.)

Course Description

This introductory course explores the exciting world of Artificial Intelligence (AI) through an engaging, accessible approach suitable for complete beginners. Leveraging hands-on activities, interactive projects, students will learn foundational concepts of AI, Machine Learning, Deep Learning, and Generative AI, including large language models, responsible AI practices, and modern generative techniques.

General Course Purpose

To provide students from diverse backgrounds with an engaging, accessible entry point into Artificial Intelligence, equipping them with foundational knowledge, practical skills, industry-recognized certifications, and enthusiasm for continued learning or career transition into AI-related fields.

Course Prerequisites/Corequisites

Basic computer literacy (using web browsers, installing software).

Course Objectives

Upon completing the course, the student will be able to:

  • Understand fundamental AI concepts, terminology, and applications
  • Explain key aspects of Machine Learning and Deep Learning
  • Understand the fundamentals of Transformers and Fine-Tuning
  • Demonstrate knowledge of modern Gen AI Architecture and Large Language Models
  • Explain the latest AI technologies, such as Agentic AI and Model-Context Protocol

Major Topics to Be Included

  • Introduction to AI and ML
  • Introduction to Neural Networks and Deep Learning.
  • Transformers and Fine-Tuning.
  • Foundation Model and Large Language Models (LLMs).
  • Generative AI, Natural Language Processing (NLP), and Retrieval Augmented Generation (RAG)
  • Agentic AI and Model-Context Protocol
  • Capstone Project: Preparation for the Google AI Essentials Certification

Required Time Allocation

Required Time Allocation per Topic To standardize the core topics of ITD 110 so that a course taught at one campus is equivalent to the same course taught at another campus, the following student contact hours per topic are required. The topics do not need to be followed sequentially. Many topics are taught best as an integrated whole, often revisiting the topic several times, each time at a higher level. There are normally 45 student-contact-hours per semester for a three-credit course. (This includes 14 weeks of instruction and does not include the final exam week so 14*3.2 = ~45 hours. Sections of the course that are given in alternative formats from the standard 15-week session still meet for the same number of contact hours.) The final exam time is not included in the timetable. The last category, Other: Optional Content, leaves time for an instructor to tailor the course to special needs or resources.

 

Topic

Hours

Percent

Introduction to AI and ML

6 hours (2 weeks)

13.33

Introduction to Neural Networks and Deep Learning

6 hours (2 weeks)

13.33

Transformers and Fine-Tuning

6 hours (2 weeks)

13.33

Foundation Model and Large Language Models (LLMs)

6 hours (2 weeks)

13.33

Generative AI, Natural Language Processing (NLP), and Retrieval Augmented Generation (RAG)

6 hours (2 weeks)

13.33

Agentic AI and Model-Context Protocol

6 hours (2 weeks)

13.33

Capstone (Google AI Essentials Certification)

6 hours (2 weeks)

13.33

Other

3 hours (1 week)

6.69

Total

45 hours

100