Study materials covering Machine Learning, NLP, AI Planning, and AI Ethics. These are the exact notes I used during my Master's program, concise and comprehensive.

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Foundational machine learning concepts including supervised learning, unsupervised learning, model evaluation, and optimization techniques.

Complete AI Planning revision notes covering blind & heuristic search (BFS, DFS, A*, Greedy), STRIPS/PDDL languages, relaxation heuristics, MDPs, reinforcement learning (Q-Learning, SARSA), Monte-Carlo Tree Search, and game theory.

Complete NLP study notes from text preprocessing to advanced transformers. Covers tokenization, embeddings, BERT, GPT, and practical applications.

Step-by-step guide to the machine learning workflow. Covers the complete ML pipeline from problem definition to model deployment and evaluation.

Critical examination of ethical considerations in artificial intelligence including bias, fairness, privacy, and responsible AI development.