Foundations of AI

Foundations of AI#

Welcome to Foundations of AI, a course about one of the most consequential technologies of our time! This course will give you an introductory and practical understanding of what artificial intelligence is, how it actually works, and why it matters for whatever field you end up in.

This Jupyter Book is the companion text for the course. It follows the thirteen units of the semester, from the origins of artificial intelligence through to questions of policy, governance, and what comes next. Every unit combines conceptual material with runnable code snippets you can copy into any IDE you prefer, though I recommend using Google Colab, as no installation or setup will be necessary.

Note

This book builds on the AI Literacy Program of the FGCU AI Academy, led by the Dendritic Institute for Human-Centered AI & Data Science. Where AI Literacy offers a gentle first encounter with AI for learners of all backgrounds, Foundations of AI goes further: it asks you to run the systems, inspect their behavior, and evaluate them critically. Several units point you back to AI Literacy modules for a lighter treatment of shared topics.

Course Information#

Course

CAI 4002 — Foundations of AI (Section 87714)

Credit Hours

3

Term

Fall 2026 — Aug 17 to Nov 24, 2026

Meetings

Monday & Wednesday, 10:30 – 11:45 AM, Holmes Engineering 402

Final

Wednesday, Dec 2, 2026, 10:00 AM – 12:15 PM (Capstone Presentations)

Instructor

Leandro de Castro, Ph.D., Full Professor

Office

ETI 112D — Office hours M W 2:00 – 4:00 PM

Who This Course Is For#

No prior technical background is required. What you do need is curiosity, a willingness to engage with new ideas, and an openness to thinking critically about the technology shaping our world. This is a course for thinkers, problem-solvers, and future professionals, whether you plan to work in technology, healthcare, business, education, law, or any other field.

You will not be asked to write software from scratch. You will be asked to run code, change it, observe what happens, and explain what you saw.

Course Learning Outcomes#

By the end of this course, you will be able to:

  1. Describe the history and evolution of artificial intelligence and explain the goals that have shaped its development.

  2. Identify and explain core AI and machine learning concepts, paradigms, and terminology.

  3. Recognize the broad range of tasks AI systems are designed to perform and the domains in which they are applied.

  4. Use AI tools effectively and apply prompt engineering techniques to achieve desired outcomes.

  5. Evaluate career pathways in AI and articulate the skills and knowledge required for AI-adjacent roles.

  6. Analyze the role of data in AI systems, including issues of quality, privacy, and bias.

  7. Critically assess the ethical, social, and policy implications of artificial intelligence in society.

  8. Propose and present an AI-based solution to a real-world problem, demonstrating integration of course concepts.

Our Journey#

The course is organized as a progressive pathway. Early units build vocabulary and methods; middle units put that vocabulary to work on real systems; later units ask what all of it means for society.

Unit

Theme

Focus

1

History and Evolution of AI

From early ideas and pioneers to the systems of today

2

Goals and Foundations of AI

What AI is trying to do: agents, problem types, terminology

3

How AI Works

The build pipeline, in plain language and in code

4

Machine Learning Paradigms

Supervised, unsupervised, reinforcement, and how models learn

5

Deep Learning and Neural Networks

Structure, training, applications, and hard limits

6

AI Applications Across Domains

Healthcare, education, law, business, finance, creative work

7

Prompt Engineering

Communicating effectively with generative systems

8

Careers in AI

Technical and non-technical pathways, and the skills behind them

9

Data, Quality and Privacy

Where data comes from, what it costs, who owns it

10

Bias and Fairness

How bias enters systems and what it does downstream

11

Ethics and Responsible AI

Principles, trade-offs, and accountability

12

Policy, Regulation and Governance

US and global frameworks, institutional roles

13

The Future of AI

Opportunities, risks, and open questions

How to Use This Book#

  • Navigate units using the left-hand sidebar or the Next/Previous buttons at the bottom of each page.

  • Look for 💡 Examples, ⚙️ Hands-On activities, 🧭 Reflection prompts, and 📘 Further Reading in every unit.

  • ⚙️ Hands-On sections contain code you can copy and run. Click the 🚀 rocket icon at the top of any page to open it in Google Colab, or paste the snippets into any Python environment you prefer (Jupyter, VS Code, Anaconda, PyCharm).

  • Every snippet in this book runs on the stock Colab environment — numpy, pandas, matplotlib, and scikit-learn are already installed. No pip install needed.

  • Read Appendix A first if you have never run Python before. It takes about ten minutes.

  • Use this book alongside Canvas, where all assignments are submitted, and the required texts.

Note

All class and assignments dates presented here are tentative and subject to change depending on the course development.

Warning

Colab tip: when you open a shared notebook, you are working in a temporary copy. Click File → Save a copy in Drive before you start, or your work will disappear when you close the tab.

Suggested Readings#

  • Russell, S. J., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. ISBN 9789356063570

  • Mitchell, M. (2020). Artificial Intelligence: A Guide for Thinking Humans. Penguin Books. ISBN 9780241404836

Course materials are delivered through FGCU First Day Ready. See the “Course Materials” link in Canvas, or visit fgcu.edu/firstdayready.

A Human-Centered Commitment#

This course embraces a human-centered approach to AI: these systems are not replacing people, they are amplifying our capacity to think, create, and solve. As you work through these units, keep returning to three questions:

What can this system actually do? What is it doing that I cannot see? Who is accountable when it is wrong?

The goal is not to make you an AI engineer in one semester. It is to make you someone who can look at an AI system, understand roughly how it works, ask the right questions about it, and make a sound judgment. That skill will outlast any particular tool.