Artificial Intelligence

The route

Lessons are in the order that makes each one easier than the last. Each unit ends with a short quiz.

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   Unit 1

   Introduction to Artificial Intelligence

   Your calculator is fast, your phone is smart — so where exactly does "intelligence" begin?
   1. Definitions of Artificial Intelligence: The Four ApproachesFree Four ways to define a clever machine — and the famous test that tries to settle the argument. 4 min
   3. Weak AI and Strong AI A chess engine can crush any grandmaster yet can't boil an egg — meet the gap between narrow AI and the dream of a true mind. 6 min
   4. Agents and Environments A Keke driver weaving through Kano traffic is a perfect agent. 3 min
   5. Rationality and Rational Agents Rational, not perfect — an agent makes the best bet with what it knows, because nobody can see the future. 3 min
   6. Unit 1 quiz5 questions on this unit 6 min
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   Unit 2 Full plan

   Intelligent Agents and Task Environments

   Before you build a brain, write its job description — and study the world it must survive in.
   1. The PEAS Framework (Performance, Environment, Actuators, Sensors) Every agent needs a job description that says how it is judged, where it works, how it acts and what it senses. 9 min
   2. Properties of Task Environments Six simple yes-or-no questions about an agent's world reveal how hard its job really is. 8 min
   3. Types of Intelligent Agents From a simple reflex to a learner that improves itself, there are five agent designs — and the world decides which one you need. 8 min
   4. Challenges of Deploying AI Systems in Nigeria From design to the dusty real world: a beautiful PEAS on paper can still die in the field. 3 min
   5. Unit 2 quiz7 questions on this unit 9 min
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   Unit 3 Full plan

   Uninformed Search Strategies

   How does a machine find the cheapest route when it has no idea where the goal is?
   1. Problem Formulation and State Space Search Before a machine can search, it must turn a messy task into states, actions, a goal test and a path cost. 5 min
   2. Tree Search, Graph Search and the Frontier Every search algorithm grows a search tree, and the shape of one data structure, the frontier, decides everything. 3 min
   3. Breadth-First Search (BFS) Knock on every door on the ground floor before climbing a single stair — that's how breadth-first search finds the nearest goal. 4 min
   4. Depth-First Search (DFS) Down the rabbit hole: plunge down one branch to the very bottom, then back up and try the next. 4 min
   5. Uniform Cost Search (UCS) Cheapest first: when roads have different lengths, count the cost, not the steps. 7 min
   6. Unit 3 quiz7 questions on this unit 9 min
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   Unit 4 Full plan

   Informed (Heuristic) Search and Adversarial Search

   Give a search algorithm a good guess about where the goal is, and it stops wandering and starts heading home.
   1. Heuristic Functions and Informed Search An okada rider who points "that side" is using a heuristic, a cheap guess at how far the goal still is. 3 min
   2. Greedy Best-First Search Follow your nose: the most impatient way to use a hunch is to always expand the node that looks closest to the goal. 3 min
   3. A* Search Add the cost you've already paid to a guess of what's left, and you get the most famous search algorithm in AI. 5 min
   4. Admissible and Consistent Heuristics When can a hunch be trusted? A* is only as trustworthy as the honesty of its guess. 6 min
   5. Adversarial Search: Minimax and Alpha–Beta Pruning What changes when the world fights back, and an opponent picks every move to make you lose? 4 min
   6. Unit 4 quiz5 questions on this unit 6 min
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   Unit 5 Full plan

   Logic and Knowledge Representation

   How do you write "if the generator smokes and keeps stalling, the air filter may be blocked" so precisely that a computer can use it?
   1. Knowledge-Based Agents Agents that know things keep a notebook of facts and rules, and a careful reader that only ever follows logic. 3 min
   2. Propositional Logic and Truth Tables Statements that are simply true or false, glued together with five little connectives. 3 min
   3. Translating English Sentences into Propositional Logic Unless, only if, neither, not both — everyday English hides traps that precise logic will not forgive. 14 min
   4. First-Order Predicate Logic and Quantifiers Objects, relations and two quantifiers let one rule speak about everyone at once. 7 min
   5. Unit 5 quiz5 questions on this unit 6 min
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   Unit 6 Full plan

   Expert Systems

   What if a village clinic with no doctor could still borrow a specialist's reasoning?
   1. Introduction to Expert Systems MYCIN matched specialist doctors in the 1970s yet never treated a patient, so what exactly is an expert system? 4 min
   2. Architecture and Components of an Expert System Inside the machine: six components, each with one job, and two of them are the heart of every expert system. 5 min
   3. Forward Chaining (Data-Driven Reasoning) Start from what you know and let the rules fire one by one until a conclusion falls out. 3 min
   4. Backward Chaining (Goal-Driven Reasoning) Start from the answer you suspect, then hunt for the facts that prove it. 5 min
   5. Advantages, Limitations and Uncertainty in Expert Systems Why expert systems shone, why they faded, and how fuzzy logic copes with a world that isn't black and white. 3 min
   6. Unit 6 quiz5 questions on this unit 6 min

Practise and remember