Introduction to Intelligent Systems
W3Schools-style exam notes covering the full course reading — short pages, clear definitions, and key takeaways.
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Chapter 1IntroductionIntelligence, learning, reasoning
Chapter 2StatisticsMeans, SE, confidence intervals
Chapter 3AlgorithmsComplexity and Big O
Chapter 4Symbolic AILogic, rules, search
Chapter 5Data representationBits, scales, text features
Chapter 6Machine learningTask, data, loss
Chapter 7Features & clusteringEngineering and k-means
Chapter 8OptimizationGradients and SGD
Chapter 9Neural networksNeurons, training, autodiff
Chapter 11Reinforcement learningMDPs, value iteration, Q-learning
Chapter 12CausalityCorrelation ≠ causation
Chapter 13FairnessParity, odds, tradeoffs
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How to use these notes
Each page is one exam-sized concept: definition boxes, formulas (KaTeX), worked examples, and takeaways. Search the sidebar to jump topics quickly.