In the last two decades, the huge advances of artificial intelligence and machine learning (ML) algorithms have opened the door to incredible opportunities to the increased complexity experiments in physics and medical applications. The use of sophisticated models, once possible only with high commitment of resources and manpower, is now available off the shelf even to smaller groups and individuals. The ALPACA (modern ALgorithms in machine learning and data analysis: from medical Physics to research with ACcelerAtors and in underground laboratories) workshop aims to bring together researchers from different fields of fundamental and medical physics to share new ideas in development and deployment of algorithms, machine learning (ML) and data analysis techniques. The workshop will cover the physics-cases and the gains obtained with the use of ML, from the boosted decision tree and self-organizing maps used by the J-PET collaboration in Kraków, to the generative adversarial networks for event simulation at the LHC, passing to variational autoencoders for underground experiments. Hands-on sessions will also be organized.