# Join Me in UC Berkeley's RDI Advanced LLM Agents MOOC

## Learning the Why Behind the How

My previous article talks to [AI Agents going mainstream in 2025](https://builditdeploy.hashnode.dev/agentic-ai-agents-go-mainstream-in-2025-with-coherent-persistence). Part of this series is explaining the ***Why Behind the How***. As you progress through your AI/ML learning journey, you will need to maintain some level of understanding as to where the larger corpus of Machine Learning and Artificial Intelligence research is progressing.

[UC Berkeley’s RDI](https://rdi.berkeley.edu) is providing such an opportunity for all of us through their [Advanced Large Language Model Agents MOOC](https://llmagents-learning.org/sp25). Their mission:

> The Berkeley Center for Responsible, Decentralized Intelligence (RDI) is a new multi-disciplinary campus-wide initiative, focusing on advancing the science, technology and education of decentralization and empowering a responsible digital economy. The RDI Center currently includes faculty and students from computer science, finance/economics, and law, and will support 3 pillars: research, education, and community / entrepreneurship.

## What You’ll Learn

From January to April, you'll learn from world-class researchers at the forefront of AI innovation. You will learn inference-time techniques from Xinyun Chen at Google DeepMind, reasoning strategies from Jason Weston at Meta, and agent safety and security from Dawn Song at UC Berkeley among many other lectures.

### Key Learning Outcomes

By the end of this course, you'll understand:

* Advanced inference and post-training techniques
    
* Agentic workflow and tool use
    
* Functional calling strategies
    
* Techniques for mathematical reasoning and theorem proving
    
* Methods for code generation and verification
    
* Inference-time techniques for reasoning
    
* Post-training methods for reasoning
    
* Search and planning
    
* Agentic workflow, tool use, and functional calling
    
* LLMs for code generation and verification
    
* LLMs for mathematics: data curation, continual pretraining, and finetuning
    
* LLM agents for theorem proving and autoformalization
    

## Lesson Schedule

The [weekly livestream](https://www.youtube.com/@BerkeleyRDI) meets on Mondays 4pm-6pm PT and 7pm-9pm ET January through April 2025. It's not too late to join. 😄

The current schedule is provided below (clickable screenshot, I'll update with a markdown table soon):

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1738624986683/cbca527d-ce07-4810-8501-bfdd2b13cac1.png align="center")

## Complete Coursework for a Certificate

You can participate through the [Application Track or Research Track](https://llmagents-learning.org/slides/llm-agents-berkeley-intro-sp25.pdf), which also involves weekly quizzes to apply for a course certificate.

### Application Track:

* 3-4 students per group
    
* Focus on applied use cases of LLMs
    
* Does not necessarily need to contribute novel research
    

### Research Track:

* 2-3 students per group
    
* Conduct novel research under the supervision of postdocs and graduate students
    
* Goal of publishing in a workshop or conference
    
* Students must apply to participate via a forthcoming Google Form
    

## Audit to Learn

You can also simply follow along at your own pace with the weekly lectures and supplemental readings. You will not receive a certificate for this option.

Whatever your choice, [please let me know](https://www.linkedin.com/in/james-thompson-ai-engineer/) if you want to work together on the Application Track or if I can help in any way. Good luck!