AI Co-Learning

Large Language Models (LLMs) have profoundly reshaped the landscape of artificial intelligence. This course is designed to guide learners through the core principles of LLMs and intelligent agents, combining theoretical foundations with hands-on experience.

We will explore:

The architecture and capabilities of large language models
The key features needed to automate real-world tasks
The underlying framework for building and deploying agent systems

In addition to foundational knowledge, we will delve into practical applications, including:

Code generation
Robotic control
Web process automation

We will also examine the limitations and risks of current LLM agents — such as hallucination, misalignment, and brittleness — and discuss how future systems might address these challenges.

Topics Covered

Foundation of LLMs
LLM Inference & Reasoning
Agent Overview (Planning – Tool Use – Memory)
MCP (Modular Control Protocol) & A2A (Agent-to-Agent communication)
Reinforcement Learning Foundations (MDPs, value functions, policy gradients)
RL for Language Models: Tool use, exploration, and long-horizon reasoning
RL in Practice: Code LLMs, experience data collection, and decentralized AI systems
Alignment via RLHF (Reinforcement Learning from Human Feedback)