Machine Learning - systems that learn patterns from data to make predictions or decisions, Deep Learning - neural network methods that use many layers to model complex patterns, Natural Language Processing - techniques that help computers understand and generate human language, Computer Vision - methods that enable machines to interpret images and video, Robotics - the design of intelligent machines that can sense, act, and perform tasks, Expert Systems - rule-based programs that mimic decision-making by human specialists, Reinforcement Learning - training agents to act through rewards and penalties, Speech Recognition - converting spoken language into text or commands, Knowledge Representation - structuring information so machines can reason with it, Automated Planning - creating sequences of actions to achieve specific goals, Generative AI - models that create new text, images, audio, or other content, Fuzzy Logic - reasoning with approximate values instead of strict true-or-false rules, Evolutionary Computing - optimization methods inspired by natural selection and genetics, Multi-Agent Systems - studying how multiple intelligent agents interact and cooperate, Recommendation Systems - suggesting relevant items based on user behavior or preferences, Affective Computing - systems that detect, interpret, or simulate human emotions, Transfer Learning - reusing knowledge from one task to improve performance on another, Autonomous Systems - machines or software that operate with limited human control, Data Mining - discovering useful patterns and relationships in large datasets, Explainable AI - designing models whose decisions can be understood by humans

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