AI Foundations
Search, logic, knowledge, uncertainty and the ideas that started the field.
- 01What Is Artificial Intelligence? A Rigorous IntroductionWe open the course by asking the hardest question first — what do we actually mean by "intelligence" in a machine? Four classical definitions, one working definition, and a map of the field.
- 02A History of AI: From Turing to TransformersSeventy years of ambition, disappointment and breakthroughs. Understanding the history of AI teaches you why today's methods look the way they do — and why humility is a scientific virtue.
- 03The Turing Test and Its CriticsTuring replaced "Can machines think?" with a game. We examine the imitation game, the Chinese Room argument, and what modern AI evaluation has learned from seventy years of debate.
- 04Intelligent Agents, Environments and the PEAS FrameworkThe agent is the central abstraction of AI. We define agents formally, characterise environments along six dimensions, and study the architectures from simple reflex agents to learning agents.
- 05Uninformed Search: BFS, DFS, Uniform-Cost and Iterative DeepeningMany AI problems reduce to finding a path in a huge graph. We formalise search problems and analyse the classic blind strategies for completeness, optimality, time and space.
- 06Informed Search: Greedy Best-First and A* with Admissible HeuristicsKnowledge about where the goal lies transforms search. We derive A*, prove its optimality with admissible heuristics, and learn how to invent good heuristics by relaxing problems.
- 07Adversarial Search: Minimax and Alpha–Beta PruningWhen an opponent is trying to defeat you, search must account for their choices. We derive minimax, prove alpha–beta pruning correct, and see how real game engines evaluate positions.
- 08Constraint Satisfaction Problems: Backtracking, Propagation and HeuristicsTimetabling, map colouring, Sudoku and circuit layout share a structure. CSPs exploit that structure with backtracking, variable-ordering heuristics and constraint propagation such as AC-3.
- 09Propositional Logic for AI: Syntax, Semantics and InferenceLogic gives an agent a language for knowledge and a mechanical way to draw conclusions. We cover syntax, truth tables, entailment, resolution and the SAT problem that powers modern solvers.
- 10First-Order Logic: Objects, Relations, Quantifiers and UnificationFirst-order logic lets us talk about objects and relations with quantifiers. We study its syntax and semantics, unification, generalised modus ponens, and resolution-based theorem proving.
- 11Knowledge Representation: Semantic Networks, Frames, Ontologies and Knowledge GraphsHow should an intelligent system store what it knows? We compare semantic networks, frames, description logics and modern knowledge graphs, and discuss the trade-off between expressiveness and tractability.
- 12Expert Systems and Rule-Based AI: Rise, Fall and LegacyExpert systems were AI's first commercial success. We build a small rule engine, examine certainty factors from MYCIN, and ask why rule-based systems remain useful — and where they fail.
- 13Bayesian Networks: Reasoning Under UncertaintyA Bayesian network encodes a joint probability distribution compactly using conditional independence. We learn the semantics, d-separation, exact inference by enumeration and variable elimination, and approximate sampling.
- 14Hidden Markov Models: Filtering, Smoothing and the Viterbi AlgorithmWhen the world changes over time and we only see noisy observations, Hidden Markov Models let us infer what is really happening. We derive the forward algorithm, Viterbi decoding and Baum–Welch learning.
- 15Classical Planning: STRIPS, PDDL and Planning GraphsPlanning is search with structured, factored states. We represent actions with preconditions and effects, compare forward and backward planning, and see how domain-independent heuristics are derived automatically.
- 16Symbolic vs Connectionist AI — and the Neuro-Symbolic SynthesisFor decades AI was split between those who manipulate symbols and those who train networks. We compare the two paradigms honestly and examine how modern research tries to combine their strengths.
- 17Local Search: Hill Climbing, Simulated Annealing and Beam SearchWhen only the final configuration matters, we can abandon paths and move through the space of complete solutions. Local search is simple, memory-light and the conceptual ancestor of gradient descent.
- 18Genetic Algorithms and Evolutionary ComputationNature optimises through selection, crossover and mutation. We implement a genetic algorithm from scratch, discuss the schema theorem and survey evolution strategies and neuroevolution.
- 19Fuzzy Logic: Reasoning with Degrees of TruthIs 29°C "hot"? Fuzzy logic replaces true/false with degrees of membership. We build a Mamdani fuzzy controller step by step: fuzzification, rule evaluation, aggregation and defuzzification.
- 20Monte Carlo Tree Search: The Algorithm Behind Superhuman GoWhen the game tree is too vast and positions too hard to evaluate, MCTS builds an asymmetric tree guided by random simulations and the UCB1 bandit formula. We implement it and connect it to AlphaZero.
- 21Decision Theory: Utility, Expected Value and the Value of InformationRational agents must act under uncertainty. We develop utility theory from axioms, the principle of maximum expected utility, decision networks and the value of perfect information.
- 22Swarm Intelligence: Particle Swarm Optimisation and Ant Colony OptimisationAnts find shortest paths and birds flock without a leader. We study how simple local rules produce intelligent collective behaviour and implement PSO and ACO for optimisation problems.
- 23Narrow AI, General AI and Superintelligence: Separating Science from SpeculationWhat would it mean for AI to be "general"? We define narrow and general intelligence, examine how to measure generality, and discuss the arguments about superintelligence with scientific care.
- 24The Landscape of Modern AI: A Map for StudentsA guided map of today's AI ecosystem — the paradigms, the tools, the research frontiers and how the tracks of this course fit together — so you always know where you are in the journey.