#Theory
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- Information Theory: A Learning Path from Probability Distributions to Coding
- Control theory: from feedback to verifiable dynamics
- Feedback and control
- Dynamic models
- Response and stability
- PID control
- Transfer functions and poles
- Frequency response and stability margins
- Sampling and digital control
- Constraints and robustness
- Control in computing systems
- Advanced: state feedback and observers
- Advanced: optimal control and MPC
- Queueing theory: reading route
- System boundaries and queueing timelines
- Little’s law and measurement boundaries
- Arrival processes and model assumptions
- M/M/1: utilization and tail latency
- Variability, M/G/1 and Kingman’s approximation
- Multiple servers and resource pooling
- Scheduling, head-of-line blocking and fairness
- Finite queues and admission control
- Queueing networks and bottlenecks
- Measurement, load testing and discrete-event simulation
- Capacity planning and overload recovery
- Optional: Conditional Information and Data Processing
- Entropy and Information Measures
- KL Divergence and Cross-Entropy
- Source Coding and Compression
- Channel Capacity and Coding
- Rate–Distortion and the Information Bottleneck
- Information Theory in LLMs