Classical AI — the foundations of modern agents: search, knowledge representation, planning, and reinforcement learning.
Never written code? You can still start here.
Module 0 opens with 18 lessons of programming from nothing — your first program, variables, loops, functions, reading errors, the terminal, Git and arrays — before any of the Artificial Intelligence material. No prior coding assumed.
AI Foundations — optional basics (free)
Week 0 · 20 lessons
Beyond the chatbox: what AI assistants really are
How the model "thinks" — and why it changes how you ask
The capability map: what AI does brilliantly
The limits: hallucination, freshness, and what never to trust it with
Your first real win: turn a 30-minute task into 3
6 senior modules · 30lessons · capstone & certificate. Upgrade your plan to unlock the advanced tier.
Advanced Search & Optimisation
5 lessons
Simulated Annealing & Cooling Schedules
Genetic Algorithms & Evolutionary Strategies
Get started
Take the free assessment to see your skill level and get a personalised learning plan.
Take AssessmentFeatured Projects
Pathfinding Visualiser
8-Puzzle Solver
Tic-Tac-Toe AI
About
Artificial Intelligence
20 questions · ~30 minutes
Context is everything: give it what it needs to know
Give it a role and a goal
Constraints and format: define what "good" looks like
Show, don't tell: examples and templates
Iterate like a pro: steer, don't restart
Documents and reports: summarise, extract, and draft better
Spreadsheets and data: Excel formulas, analysis, and charts
Presentations: from blank page to a solid deck
Long, messy input: transcripts, threads, notes, and screenshots
ChatGPT, Claude, Copilot, Gemini: which to reach for, and when
Make smarter decisions: AI as your thinking partner
Chaining steps: multi-part tasks done reliably
Your prompt library: reusable templates that save hours
Staying safe: privacy, accuracy, and company data
Capstone: do a real task end-to-end (graded) → your certificate
Module 0 — Foundations: Logic, Probability & Algorithms for AI
Week 0 · 23 lessons
What a program is and how to run your first one
Storing values in variables, and why text and numbers behave differently
Making decisions with if, else and comparisons
Storing many values in a list
Looking things up by name with dictionaries
Doing something to every item with loops
Writing your own functions
Reading errors without panic
Finding the mistake in your own code
Saving your code in a file and running it
Importing code other people have written
Giving each project its own environment
Talking to your computer with the terminal
Saving your work with Git
Putting your work on GitHub
What an array is, and why a list is not enough
Picking out pieces: indexing and slicing, including two dimensions
Maths on a whole array at once, and where you go next
Logic & Sets for AI
Probability for AI
Graphs, Trees & Search Spaces
Algorithms & Complexity: BFS, DFS & Big-O
Python & Git for AI
AI Foundations
Week 1 · 5 lessons
History & Scope of AI
Search Problems & State Spaces
Breadth-First & Depth-First Search
Uninformed vs Informed Search
Problem Formulation
Heuristic Search
Week 2 · 5 lessons
A* Search Algorithm
Heuristic Design
Greedy Best-First Search
Local Search & Hill Climbing
Simulated Annealing
Adversarial Search
Week 3 · 5 lessons
Game Theory Basics
Minimax Algorithm
Alpha-Beta Pruning
Monte Carlo Tree Search
Game-Playing AI
Knowledge Representation
Week 4 · 5 lessons
Propositional Logic
First-Order Logic
Ontologies & Taxonomies
Knowledge Graphs
Reasoning Systems
Constraint Satisfaction
Week 5 · 5 lessons
CSP Formulation
Backtracking Search
Arc Consistency
Constraint Propagation
Real-World CSPs
Planning & Reasoning
Week 6 · 5 lessons
STRIPS Planning
PDDL Language
Classical Planning
Hierarchical Planning
Planning Under Uncertainty
Probabilistic AI
Week 7 · 5 lessons
Bayesian Networks
Probabilistic Inference
Hidden Markov Models
MCMC Methods
Decision Making Under Uncertainty
Reinforcement Learning
Week 8 · 5 lessons
RL Fundamentals
Q-Learning
Policy Gradients
Deep Q-Networks
RL Applications
Ant Colony & Swarm Intelligence
Constraint Satisfaction & Arc Consistency
Hybrid & Memetic Algorithms
Multi-Agent Systems
5 lessons
Agent Architectures & BDI Model
Communication & Coordination Protocols
Game-Theoretic Multi-Agent Interactions
Auction Mechanisms & Mechanism Design
Emergent Behaviour & Swarm Robotics
Advanced Probabilistic Reasoning
5 lessons
Hidden Markov Models & the Viterbi Algorithm
Kalman Filters & State Estimation
Particle Filters & Sequential Monte Carlo
Markov Decision Processes & Value Iteration
POMDPs & Belief-Space Planning
Deep Reinforcement Learning
5 lessons
Policy Gradient Methods & REINFORCE
Actor-Critic & Advantage Estimation
PPO & Trust Region Methods
SAC & Maximum Entropy RL
Model-Based RL & World Models
Neuro-Symbolic AI
5 lessons
Neural Network Refresher for Symbolic Integration
Differentiable Programming & Soft Logic
Knowledge Graph Embeddings & Reasoning
Neural Theorem Proving & Program Synthesis
Hybrid Architectures & Concept Learning
AI Safety & Alignment
5 lessons
Reward Hacking & Specification Gaming
Scalable Oversight & RLHF
Interpretability & Mechanistic Analysis
Robustness, Adversarial Attacks & Distributional Shift
Value Alignment & Responsible Deployment