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1. Foundations of AI Students begin by exploring the history and evolution of AI, from early symbolic logic systems to modern machine learning breakthroughs. They learn core definitions, such as the difference between narrow AI (task-specific systems) and general AI (hypothetical human-like intelligence), and examine ethical considerations like bias, privacy, and societal impact. 2. Problem-Solving with AI The course introduces search algorithms (e.g., breadth-first, A*) and optimization techniques used in problem-solving, such as pathfinding or puzzle-solving. Students also study logic-based systems, including propositional and predicate logic, to understand how AI models reasoning and decision-making. 3. Machine Learning Basics A major focus is on machine learning (ML), the engine behind most modern AI. Students learn: Supervised learning: Training models with labeled data (e.g., regression, classification). Unsupervised learning: Discovering patterns in unlabeled data (e.g., clustering). Reinforcement learning: Teaching agents to make decisions via rewards/punishments. Key algorithms like decision trees, k-nearest neighbors (k-NN), and neural networks. 4. Neural Networks and Deep Learning Students dive into artificial neural networks (ANNs), inspired by biological brains. They explore: Basic architecture (layers, neurons, activation functions). Deep learning concepts, including convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for sequential data (e.g., language). Applications like facial recognition, speech synthesis, and autonomous driving. 5. Natural Language Processing (NLP) The course covers how AI interacts with human language. Topics include: Text preprocessing (tokenization, stemming). Sentiment analysis, chatbots, and machine translation. Tools like transformer models (e.g., GPT, BERT) that power modern NLP. 6. Ethics and Societal Impact Students critically analyze AI’s role in society, discussing: Algorithmic bias and fairness. Job displacement and economic shifts. Regulatory challenges and responsible AI development. 7. Hands-On Projects Practical experience is emphasized through coding exercises using frameworks like Python, TensorFlow, or PyTorch. Projects might involve building a simple chatbot, training an image classifier, or implementing a recommendation system. Outcomes By the end of the course, students will: Understand core AI methodologies and their limitations. Differentiate between AI, ML, and deep learning. Apply basic algorithms to real-world problems. Critically evaluate AI’s ethical implications. Gain a foundation for advanced study or careers in AI-driven industries.

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