# 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](https://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.