This two-week course will provide an introduction to basic techniques in Machine Learning (ML). The programme is aimed at advanced undergraduate STEM students. We will follow a “learn by doing” philosophy with plenty of exercises for the participants to solve. The course will end with a challenge where teams of students will use their acquired knowledge and compete to identify rare decays in real Large Hadron Collider (LHC) data. 
More about our PWF projects in Guatemala.

 Course contents: 

• Basics of statistical learning 
• Python refresher: NumPy, Pandas, Matplotlib 
• Bayesian statistics 
• Regression and classification 
• Model selection 
• Decision trees and random forests 
• Support Vector Machines 
• Feedforward perceptron 
• Neural networks 

Location: Universidad del Valle de Guatemala 

Dates: November 25-December 6, 2019 

Lecturers: Kurt Rinnert (U. Liverpool and CERN) and Giovanni Ramirez (USAC Guatemala) 

Scientific committee: Juan Diego Chang, Kate Shaw, Álvaro Véliz Osorio 

 Course contents: 

• • Basics of statistical learning 
• • Python refresher: NumPy, Pandas, Matplotlib 
• • Bayesian statistics 
• • Regression and classification 
• • Model selection 
• • Decision trees and random forests 
• • Support Vector Machines 
• • Feedforward perceptron 
• • Neural networks