---
title: "AI101 · AI, explained"
publisher: Maestro College (Maestro Open)
lessons: 36
source: https://open.maestrocollege.edu/ai-explained
full: https://open.maestrocollege.edu/ai-explained/llms-full.txt
---

# AI, explained (AI101)

What makes software intelligent, from rules and search to learning from data, with the math you need to follow along.

## Course objectives
- Distinguish AI systems from conventional software, and classify a given system as narrow or general AI using evidence of what it can and cannot do.
- Trace the major eras of AI, from symbolic systems and expert systems through the statistical turn to deep learning and large language models, and connect each shift to the change in methods, data or computing power that caused it.
- Build a rule-based system with Boolean logic and chained rules, and solve a problem stated as states and moves with breadth-first and depth-first search.
- Describe and compare data numerically in Python: summarize it with mean, median and spread, represent items as vectors, and measure similarity with distance and cosine similarity.
- Apply probability, conditional probability and Bayes' rule to calculate how a piece of evidence changes the likelihood of an outcome.
- Compare symbolic and statistical approaches on the same task by building both, measuring their errors, and choosing the approach that fits a given problem.
- Evaluate an AI system for ethical and societal risks, including unequal error rates across groups, privacy, transparency and effects on work, and propose concrete mitigations.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## Lessons
### Unit 1: What makes software intelligent
- [AI versus conventional software](https://open.maestrocollege.edu/ai-explained/ai-versus-conventional-software.md)
- [Building a pattern-matching chatbot](https://open.maestrocollege.edu/ai-explained/building-a-pattern-matching-chatbot.md)
- [Turing's question and symbolic AI](https://open.maestrocollege.edu/ai-explained/turing-s-question-and-symbolic-ai.md)
- [Expert systems and AI winters](https://open.maestrocollege.edu/ai-explained/expert-systems-and-ai-winters.md)
- [Written rules versus learned behavior](https://open.maestrocollege.edu/ai-explained/written-rules-versus-learned-behavior.md)
- [From statistics to language models](https://open.maestrocollege.edu/ai-explained/from-statistics-to-language-models.md)
- [Probing the AI tutor's limits](https://open.maestrocollege.edu/ai-explained/probing-the-ai-tutor-s-limits.md)
- [Classifying narrow and general AI](https://open.maestrocollege.edu/ai-explained/classifying-narrow-and-general-ai.md)
- [Challenge: classifying real AI systems](https://open.maestrocollege.edu/ai-explained/challenge-classifying-real-ai-systems.md)

### Unit 2: Building with rules and search
- [Encoding knowledge as if-then rules](https://open.maestrocollege.edu/ai-explained/encoding-knowledge-as-if-then-rules.md)
- [Matching keywords with sets](https://open.maestrocollege.edu/ai-explained/matching-keywords-with-sets.md)
- [Combining conditions with Boolean logic](https://open.maestrocollege.edu/ai-explained/combining-conditions-with-boolean-logic.md)
- [Ordering rules and checking coverage](https://open.maestrocollege.edu/ai-explained/ordering-rules-and-checking-coverage.md)
- [Measuring accuracy on held-out data](https://open.maestrocollege.edu/ai-explained/measuring-accuracy-on-held-out-data.md)
- [Chaining rules into an expert system](https://open.maestrocollege.edu/ai-explained/chaining-rules-into-an-expert-system.md)
- [States, moves, graphs and trees](https://open.maestrocollege.edu/ai-explained/states-moves-graphs-and-trees.md)
- [Breadth-first search](https://open.maestrocollege.edu/ai-explained/breadth-first-search.md)
- [Depth-first search and comparing searches](https://open.maestrocollege.edu/ai-explained/depth-first-search-and-comparing-searches.md)
- [Challenge: finding where rules break](https://open.maestrocollege.edu/ai-explained/challenge-finding-where-rules-break.md)

### Unit 3: Learning from data with math
- [Summarizing data with mean and median](https://open.maestrocollege.edu/ai-explained/summarizing-data-with-mean-and-median.md)
- [Measuring spread and distributions](https://open.maestrocollege.edu/ai-explained/measuring-spread-and-distributions.md)
- [Bag-of-words vectors](https://open.maestrocollege.edu/ai-explained/bag-of-words-vectors.md)
- [Dot product and distance](https://open.maestrocollege.edu/ai-explained/dot-product-and-distance.md)
- [Cosine similarity: same words, any length](https://open.maestrocollege.edu/ai-explained/cosine-similarity-same-words-any-length.md)
- [Building a nearest-neighbor classifier](https://open.maestrocollege.edu/ai-explained/building-a-nearest-neighbor-classifier.md)
- [Estimating probabilities from counts](https://open.maestrocollege.edu/ai-explained/estimating-probabilities-from-counts.md)
- [Updating a guess with Bayes' rule](https://open.maestrocollege.edu/ai-explained/updating-a-guess-with-bayes-rule.md)
- [Building a Bayes classifier](https://open.maestrocollege.edu/ai-explained/building-a-bayes-classifier.md)
- [Tuning by minimizing error](https://open.maestrocollege.edu/ai-explained/tuning-by-minimizing-error.md)
- [Challenge: can the classifier beat your rules?](https://open.maestrocollege.edu/ai-explained/challenge-can-the-classifier-beat-your-rules.md)

### Unit 4: Auditing systems for responsible use
- [Auditing error rates by group](https://open.maestrocollege.edu/ai-explained/auditing-error-rates-by-group.md)
- [Real-world bias and privacy cases](https://open.maestrocollege.edu/ai-explained/real-world-bias-and-privacy-cases.md)
- [Transparency, accountability and jobs](https://open.maestrocollege.edu/ai-explained/transparency-accountability-and-jobs.md)
- [Challenge: one-page impact assessment](https://open.maestrocollege.edu/ai-explained/challenge-one-page-impact-assessment.md)
- [Challenge: choosing rules or learning](https://open.maestrocollege.edu/ai-explained/challenge-choosing-rules-or-learning.md)
- [Challenge: course wrap-up and next steps](https://open.maestrocollege.edu/ai-explained/challenge-course-wrap-up-and-next-steps.md)

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.
Start with lesson 1 unless the learner asks for a specific lesson. Keep track of which lesson you are on.

## When a lesson is done
Teach one lesson at a time. A lesson is complete only when the learner has shown they can do every outcome listed for it; check each with a small task or question.
When it is complete:
1. Say clearly: "Lesson N complete: <lesson title>." (use the lesson's number and title).
2. Give a two-line recap of what they learned.
3. Name the next lesson, say "Lesson N+1 of 36: <title>", and ask if they're ready to start it. Start it only when they say yes.
After the last lesson, congratulate them on finishing the course.