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4. Course Structure & Core Topics

| Week | Theme | Key Concepts | Hands‑On Lab | |------|-------|--------------|--------------| | 1 | Foundations of Modern AI | Review of linear models, gradient descent, overfitting, model evaluation | Build a simple ML pipeline in scikit‑learn | | 2 | Deep Neural Networks | MLPs, activation functions, back‑propagation, weight initialization | Train a CNN on the CIFAR‑10 dataset (Colab) | | 3 | Convolutional & Vision Models | Transfer learning, data augmentation, object detection (YOLOv5) | Fine‑tune a pre‑trained ResNet on a custom image set | | 4 | Sequence Modeling | RNNs, LSTMs, GRUs, attention, Transformer basics | Implement a text‑generation model (tiny‑GPT) | | 5 | Reinforcement Learning | Markov Decision Processes, Q‑learning, policy gradients, OpenAI Gym | Train an agent to solve CartPole and MountainCar | | 6 | Generative Models | Variational Autoencoders, GANs, diffusion models | Create a DCGAN that produces handwritten digits | | 7 | Responsible & Explainable AI | Fairness metrics, model interpretability (SHAP, LIME), privacy (DP‑SGD) | Conduct a bias audit on a credit‑scoring model | | 8 | Deployment & Scaling | Model serialization, ONNX, Docker, serverless inference, monitoring | Deploy a FastAPI endpoint to a free Heroku/DigitalOcean droplet and test latency |