The fields of large scale data analytics and machine learning have made impressive progress in recent years. Many applications have been successful in applying techniques in these fields for problems in areas such as health, language processing, search engines, etc Many tools have been developed to facilitate the application of these techniques (e.g. libraries like Scikit-learn, TensorFlow, Keras, PyTorch, etc or frameworks like Apache Spark, Caffe, etc)
Although some examples exist of applications in accelerators and experimental physics installations, there is a feeling that we could benefit more from these methods and tools. The workshop is intended to give a tutorial introduction to machine learning and to bring up discussions on experiences and possible applications of advanced data science and machine learning techniques to experimental physics facilities.
The workshop will last one full day. In the morning, introductory tutorials to machine learning will be presented. In the afternoon, speakers are welcome to share their experience with presentations/demonstrations of solutions that worked or didn’t worked well. A final discussion will take place on possible next steps.
Correlated topics: data analytics, statistical analysis, data mining, deep learning, neural networks, expert systems, automatic optimization, robotics, etc.
Invited lecturers: Alfredo Canziani (New York University), Gianluca Valentino (University of Malta)
ICALEPCS 2019 Official Page: https://icalepcs2019.bnl.gov
ICALEPCS 2019 Workshops: https://icalepcs2019.bnl.gov/workshops.html#12
