AI Fundamentals: From Zero to Hero
The big picture of artificial intelligence
About this course
Understand what AI really is, how it learns, where it's used today, and what's coming next. No math gatekeeping — just clarity, examples, and confidence.
What you'll learn
- Differentiate AI, ML, and Deep Learning with real examples
- Map common AI use-cases across industries
- Recognise hype vs reality in modern AI
- Speak confidently about model training and inference
Curriculum
Dr. Pamela Howard leads the introductory program at United Tribes University — the curriculum every UTU student walks through first. Her conviction: in 2026, fluency in AI is a basic civic skill, not a specialist's hobby. The AI track she's shaping is short on theatre and long on practice — labs every lesson, an AI tutor in the room, assignments a working professional would respect.
Build an AI Use-Case One-Pager
Pick a real problem from your work or life. Write a one-page document that: 1. Names the problem in plain English (2-3 sentences). 2. Picks the AI approach that fits — supervised, unsupervised, RL, or an LLM-based agent — and **justifies why**. 3. Lists the data you'd need (be specific: rows, columns, where it lives). 4. Identifies the top 3 ways this could fail in production (hallucination? bias? drift? latency?). 5. Picks one evaluation metric you'd publish weekly. Length: ~500 words. Submit as a public link (Google Doc, Notion, or Markdown gist).
- 1Problem stated in plain language, no jargon
- 2AI approach selected and justified with one sentence
- 3Data requirements concrete (named sources, columns, scale)
- 4At least 3 realistic failure modes named
- 5Eval metric is measurable and tied to a business outcome
What graduates shipped.
Once you ship, your project lives here — linked from your public verify page so recruiters can click through.