advanced Artificial intelligence

Course Introduction & Foundations Recap

• Topics: Overview of AI domains, key ML/DL concepts, review of gradient descent,      overfitting
• Project: Train and evaluate a simple CNN/LSTM on a custom dataset
• Tools: TensorFlow / PyTorch, Jupyter

Transfer Learning & Pretrained Models

• Topics: Fine-tuning, feature extraction, domain adaptation
• Project: Fine-tune BERT or ResNet on a niche dataset
• Tools: Hugging Face Transformers, PyTorch Hub

Advanced Deep Learning Architectures

• Topics: Attention, Transformers, ViT, hybrid models
• Project: Implement a Transformer for text classification or image captioning
• Tools: PyTorch Lightning, 🤗 Datasets

Natural Language Processing with LLMs

• Topics: Prompt engineering, generative models, embeddings
• Project: Build a chatbot using OpenAI/GPT-based API or local LLM
• Tools: LangChain, OpenAI API, LLaMA or Mistral

Reinforcement Learning

• Topics: Q-learning, PPO, policy gradients, exploration strategies
• Project: Train an agent to solve a custom OpenAI Gym environment
• Tools:      Stable-Baselines3, Unity ML-Agents

Computer Vision – Detection & Segmentation

• Topics: Object detection (YOLOv8), segmentation (U-Net, Mask R-CNN)
• Project: Build an object detection model on COCO/VOC or custom images
• Tools: Ultralytics YOLO, Detectron2

Generative Models – GANs & Diffusion

• Topics: GANs, Stable Diffusion, DDPM
• Project: Train a GAN to generate art or fake faces
• Tools: StyleGAN2, Hugging Face Diffusers

Probabilistic & Bayesian Methods

• Topics:      HMMs, Bayesian inference, variational autoencoders
• Project:      Implement a VAE on MNIST or anomaly detection
• Tools:      Pyro, TensorFlow Probability

Explainable AI & Model Interpretation

• Topics: SHAP, LIME, saliency maps, XAI principles
• Project: Analyze fairness and explainability on an NLP model
• Tools: Captum, SHAP, What-If Tool

AI in Production – MLOps

• Topics: Model versioning, pipelines, monitoring, deployment
• Project: End-to-end pipeline with training → serving → logging
• Tools: MLflow, FastAPI, Docker, Weights & Biases

Federated & Edge Learning

• Topics: On-device inference, federated training, privacy
• Project: Build a mobile-optimized model with quantization
• Tools: TensorFlow Lite, PySyft, ONNX

Ethics, Safety & Future of AI

• Topics:      Bias, fairness, AI alignment, AGI debates
• Project:      Audit and mitigate bias in an AI model
• Wrap-up:      Final presentations & portfolio development