Course Staff

Prof. Dawn Song Prof. Matei Zaharia
UC Berkeley UC Berkeley

Guest lecturer & project mentor: John Whaley

GSI: Yu Gai

Reader: Shiladitya Dutta and Bill Zheng

Course Description

Generative AI and foundational models including ChatGPT have ushered the world into a new era with rich new capabilities for wide-ranging application domains. With these new capabilities also comes unprecedented challenges such as privacy, safety, ethics, alignment, and decentralization. In this class, we will introduce foundations of Large Language Models (LLMs), discuss the infrastructure, tooling, and best practices for building and running applications with LLMs, and explore the risks and challenges with these technologies and how we can build towards a responsible and democratized and decentralized future with AI. In particular, this class will cover a wide-ranging topics including:

Syllabus (subject to change)

Date Topic
Aug 27 Join The Future of Decentralization, AI, and Computing Summit!
Aug 29 No class
Sep 5 Intro (slides) & Foundations of LLM (slides)
Guest speaker: Eric Wallace, UC Berkeley
Sep 12 Infrastructure Layer I: Training and Inference, Performance Optimization, Scalability (slides)
Guest speaker: Jonathan Frankle, MosaicML
Sep 19 Infrastructure Layer II: Retrieval, Vector Databases, Search (slides)
Guest speaker: Jerry Liu, LlamaIndex (slides)
Sep 26 App Development Layer: Prompt Engineering, Chains, Tools
Oct 3 Application Domains I: Software Engineering/Code Generation, Data Science
Oct 10 Application Domains II: Security, Education
Oct 17 Agents: RPA, Virtual Assistants
Oct 24 Decentralized Training and Inference, Open-Source Models
Guest speaker: Ce Zhang, Together
Oct 31 Trustworthiness: Privacy, Hallucinations, Adversarial Attacks
Guest speaker: Nicholas Carlini, Google DeepMind
Nov 7 Decentralized Decision Making
Nov 14 Ethics and Fairness, Safety, Alignment
Nov 21 Edge compute; Federated learning; Open source data
Guest speaker: Tianqi Chen, CMU
Nov 28 Project Demos

Enrollment and Grading

Students are strongly encouraged to have had experience and basic understanding of Machine Learning and Deep Learning before taking this class, e.g., have taken courses such as CS182, CS188, and CS189. Graduate students should enroll in CS294-196. Undergraduate students should enroll in CS194-196.

If you are not able to enroll in the class, please fill out the signup form and we will get back to you as soon as possible.

This is a variable-unit course. All enrolled students are expected to participate in lectures in person. Students enrolling in one unit are expected to submit an article that summarizes either one of the lectures or the Future of Decentralization, AI, and Computing Summit. Students enrolling in more than one units are expected to submit a lab assignment and a project instead of the article. The project of students enrolling in 2 units should have a written report, which can be a survey in a certain area related to LLMs. The project of students enrolling in 3 units should also have an implementation (coding) component that programmatically interacts with LLMs, and the project of students enrolling in 4 units should have a very significant implementation component with the potential for either real world impacts or intellectual contributions. The grade breakdowns for students enrolled in different units are the following:

  1 unit 2 units 3/4 units
Participation 50% 20% 10%
Article 50%    
Lab   20% 10%
  Proposal   10% 10%
  Milestone   10% 10%
  Presentation   25% 25%
  Report   15% 15%
  Implementation     20%

Lab and Project Timeline

  Released Due
Project group formation Sep 5 Sep 19
Project proposal Sep 12 Oct 3
Lab Sep 19 Oct 17
Project milestone Oct 3 Oct 31
Project presentation Oct 31 Nov 28
Project final report Oct 31 Dec 12


Lecture: 4-6pm PT Tuesday in Physics Building 1. (Click here if you cannot find the classroom)

Office Hours

Yu Gai: 2:45-3:45pm PT Tuesday. Location to be announced on edstem.