UC Berkeley RDI Advanced LLM Agents MOOC

Learning the Why Behind the How

My previous article talks to AI Agents going mainstream in 2025. 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 is providing such an opportunity for all of us through their Advanced Large Language Model Agents MOOC. 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 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):

Complete Coursework for a Certificate

You can participate through the Application Track or Research Track, 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 if you want to work together on the Application Track or if I can help in any way. Good luck!

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

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Learning the Why Behind the How

What You’ll Learn

Key Learning Outcomes

Lesson Schedule

Complete Coursework for a Certificate

Application Track:

Research Track:

Audit to Learn

Command Palette

Comments

Agentic AI Agents

Agentic AI Agents Go Mainstream in 2025 with Coherent Persistence

More from this blog

Learning the Why Behind the How

What You’ll Learn

Key Learning Outcomes

Lesson Schedule

Complete Coursework for a Certificate

Application Track:

Research Track:

Audit to Learn