Welcome Presentation

• Yifan Chen (SLAC)
• Oz Amram (Fermilab)
• Sascha Diefenbacher (Lawrence Berkeley National Laboratory)

Introduction to Machine Learning: Part I

• Sascha Diefenbacher (Lawrence Berkeley National Laboratory)

Tutorial: Intro to Neural Networks I

• Kevin Greif (University of California, Irvine)
• Yifan Chen (SLAC)

Introduction to Machine Learning: Part II

• Dennis Noll (Lawrence Berkeley National Lab. (US))

Tutorial: Intro to Neural Networks II

• Kevin Greif (University of California, Irvine)
• Yifan Chen (SLAC)

Differentiable Programming

• Sean Gasiorowski (SLAC)

Overview of ML in particle physics

• Aishik Ghosh

**Should we include some neutrino stuff in this talk?**

Simulation-based Inference: Part I (Lecture)

• Aishik Ghosh
• Tae Park (MPP)
• Sean Benevedes (MIT)

Simulation-based Inference: Part II (tutorial)

• Aishik Ghosh
• Tae Park (MPP)
• Sean Benevedes (MIT)

Transformers and Foundation Models: Part I

• Vinicius Mikuni

Transformers and Foundation Models: Part II

• Vinicius Mikuni

LLM Application

• Thorsten Hellert (LBNL)

Generative Models: Part I

• Sascha Diefenbacher (Lawrence Berkeley National Laboratory)

Generative Models: Part II

• Sascha Diefenbacher (Lawrence Berkeley National Laboratory)

Anomaly Detection: Part I

• Oz Amram (Fermilab)

Anomaly Detection: Part II

• Oz Amram (Fermilab)

Industry talk

• Chase Shimmin (P-1)

Parallel Sessions

Efficient ML: Part I

• Elham E Khoda (University of California, San Diego)

Efficient ML: Part II

• Elham E Khoda (University of California, San Diego)

Closeout session

• Oz Amram (Fermilab)
• Sascha Diefenbacher (Lawrence Berkeley National Laboratory)
• Yifan Chen (SLAC)
• Elham E Khoda (University of California, San Diego)
• Aishik Ghosh