Jeff Liu's AI Learning Notes
Welcome! This is a comprehensive collection of AI learning notes covering computer science, machine learning, deep learning, reinforcement learning, AI engineering, and more. The content is drawn from coursework, classic textbooks, online resources, and hands-on practice.
- Logs
AI learning diary, project notes (Alice Project — generative agent world simulation)
- Computing Science
Computer science, algorithms & data structures, mathematical foundations (calculus, linear algebra, probability, information theory, statistics, automatic differentiation)
- Symbolicism
AIMA (search, logic, knowledge representation, Bayesian networks, decision theory), AI Planning (classical planning & decision-making, motion planning & trajectory optimization)
- Machine Learning
ML fundamentals, data science (EDA, feature engineering), classical supervised learning (SVM, Random Forest, XGBoost, LightGBM)
- Deep Learning
CNN, RNN/LSTM, Transformer, generative models (VAE/GAN/Diffusion), foundation models, optimization & regularization
- Reinforcement Learning
Classical RL (MDP/TD/Policy Gradient), Deep RL (DQN/PPO/SAC), Offline RL, MBRL, LLM post-training
- AI Agents
AI Agent architectures, reasoning patterns (ReAct/CoT/ToT), generative agents
- Robotics
Robotics overview, simulation & hardware platforms, embodied intelligence, intuitive physics
- Human-Like Intelligence
Philosophy of mind, neuroscience insights, world models (JEPA), causal reasoning, meta-learning
- Brain-Computer Interface
Neurophysiology, intention-to-action pipeline, neural foundation models, brain-to-language/image decoding, sensory writing, commercial/clinical & neurorights
- AI Engineering
PyTorch development, data engineering, fine-tuning, inference deployment (vLLM), MLOps, AI infrastructure
- AI Safety & Trustworthiness
Adversarial attacks & defenses, LLM jailbreaking, red teaming, explainability, robustness, privacy protection
Other notes: Finance Learning Notes →
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