TRISEP Machine Learning tutorial

Welcome to the Machine Learning Tutorial for the TRISEP HEP Summer School at TRIUMF, June 16–27, 2025! https://www.trisep.ca

This is a hands-on tutorial focused on practical applications of machine learning in high energy physics (HEP). You will actively code, experiment, and solve real-world HEP problems using modern data science tools. The sessions are designed for students and researchers eager to learn by doing—no prior machine learning experience required, though familiarity with Python and basic statistics is recommended.

Get ready to dive in, collaborate, and discover how machine learning can accelerate research in particle physics!

Setup:

• To get started, please follow the instructions in this setup guide.
• Then, fork and clone this repository with:

  git clone https://github.com/<owner of the fork>/BeginnerTutorial
  

Exercises:

• Exercise 1: Exploring the dataset
• Exercise 2: Multi-layer perceptron (MLP) for Higgs classification
• Exercise 3: Boosted Decision Tree (BDT) for Higgs classification