Math for AI Made Simple: The Linear-Algebra Lego Set Behind Every Model
Scalars, vectors, matrices and tensors, the five operations every neural network is made of, and the shape rules that cause most of the bugs.
AI Foundations01
Series · 10 parts · 2,023–2,024
AI Foundations
A run through the classical ground: linear algebra, search, adversarial search, constraint satisfaction, logic, and planning. Written for someone starting from nothing.
Machine Learning 101: PyTorch, TensorFlow & Decision Trees
What a deep-learning framework actually does for you, how a decision tree picks its questions, and which of the three to reach for on real data.
AI Foundations02
Intelligent Agents & Search: A Baby-Steps Tour from ‘What is Rational?’ to ‘How do we find the goal?’
What an agent actually is, how to turn a problem into a search problem, and why changing one line of priority code turns breadth-first search into A*.
AI Foundations03
Beyond Classical Search: Hill-Climbers, Hidden Worlds & Agents That Learn on the Fly
Hill climbing, simulated annealing, belief states and online search: what to do when A* is the wrong tool, the world hides half of itself, or there is no map yet.
AI Foundations04
Adversarial Search 101: From Minimax to AlphaZero, How AI Plans When an Opponent Fights Back
Minimax, alpha-beta pruning, expectiminimax and MCTS: how a program plans when something is planning against it, and what neural evaluation changed.
AI Foundations05
Constraint Satisfaction 101: From Sudoku Logic to Map-Coloring Zen
Arc consistency, backtracking with MRV and min-conflicts, explained through Sudoku and map colouring, plus what you would actually use today.
AI Foundations06
Logical Agents 101: Slaying the Wumpus with Pure Reason
Knowledge bases, entailment, resolution and SAT solving, learned by staying alive in the Wumpus World, with runnable PySAT code.
AI Foundations07
First-Order Logic Made Friendly: From ‘Socrates Is Mortal’ to Automatic Proofs
Quantifiers, unification, forward and backward chaining, and resolution, from Socrates to a solver that finds the proof for you.
AI Foundations08
Classical Planning 101: From STRIPS Blocks to Graphplan Magic
How STRIPS turns a to-do list into an algorithm: factored states, progression and regression, delete relaxation, planning graphs, and modern planners.
AI Foundations09
Brains Behind the Bots: Classical Planning from Ground Up
PDDL, heuristics that sometimes do nothing, HTN, temporal and multi-agent planning, with real planner numbers on Towers of Hanoi.
AI Foundations10