2025 AI Advanced Course

🧠 Core Advanced Topics Deep Learning Architectures Transformers (e.g., GPT, BERT, Vision Transformers) Generative models (GANs, VAEs, Diffusion Models) Sequence models (LSTMs, GRUs, Attention mechanisms) Self-supervised learning Reinforcement Learning (RL) Policy gradients Actor-critic methods (PPO, A3C) Deep Q-networks (DQN) Multi-agent RL Applications in robotics and game theory Natural Language Processing (NLP) Large Language Models (LLMs) Prompt engineering Fine-tuning vs. pretraining Retrieval-augmented generation (RAG) Evaluation metrics for language models Computer Vision Object detection and segmentation (YOLO, Mask R-CNN) Image generation and enhancement Vision-language models (CLIP, DALL·E) ⚙️ Practical & Technical Skills Scalable ML/AI Systems Model deployment (TensorFlow Serving, TorchServe) Using cloud platforms (AWS SageMaker, GCP AI Platform) Optimization and quantization (ONNX, TensorRT) Experimentation and MLOps CI/CD pipelines for ML Model monitoring and versioning Tools: MLflow, DVC, Weights & Biases Data-centric AI Data quality, bias, and augmentation Active learning and data labeling strategies 🧪 Research-Oriented Modules AI Ethics and Safety Bias, fairness, transparency Explainable AI (XAI) Alignment and value learning Advanced Topics in Learning Meta-learning Few-shot and zero-shot learning Causal inference in ML Curriculum learning Recent Research Papers Reading and presenting top papers (from NeurIPS, ICML, CVPR, ACL, etc.) Replicating experiments Contributing to open-source or research