Upon completion of previous schools we've asked participants to describe their experience:
"I learned a lot about different types of ML (eg: Random Forrest, Ada/Gradient Boosting, DNNs). I had much fun during the attending to the seminars in order to improve my Kaggle score."
-- Timon Schmelzer
"It met my expectations very well. Of course it will take time to implement and optimize the machine learning techniques for the analysis I'm working on, but now I have a clear idea of what to do and of the various purposes machine learning can be used in particle physics."
-- Joona Havukainen
"The school met my expectations quite well. The material was challenging but appropriate, and the data challenges were exciting."
-- Andreas Sogaard
"The MLHEP school more than met my expectations, it was a really interesting (and fun!) experience."
-- Jan Kuechler
"The school was very helpful, particuarly with the competition. It got us all really excited to try the things learned during the lectures and the seminars."
-- Jay Vora
"Now I have a clear ideas about the most popular ML techniques: how they works, what optimization problems it solves, how we can improve it, what is advantages and disadvantages. Now I understand what HEP problems ML can solve. "
-- Filatov Artem
"Theoretical presentations were complete, except maybe for a bit more insight into Deep Learning models. Practice seminars were a bit too frontal to experiment a lot of things, and follow the flow at the same time."
-- Mauro Verzetti
"It had many exercises, and the kaggle competition was somehow "affordable" (in an easy way) also for a very beginner like me."
-- Lisa Benato
"I learned a lot of useful techniques and I was able to get confident with their implementation. I learned a lot also from the invited talks describing possible applications to a variety of analysis cases."
-- Giovanni Siragusa
"The MLHEP school fully met my expectations. I now have a better fundamental understanding of the field with lots of resources for improving my knowledge and skills."
-- Daniel Marley
"I surely learned a lot, I now have enough background to dig deeper into various ML techniques and hopefully use them in my regular tasks."
-- Alina Eksaeva
"I liked almost everything: the location was fantastic, selection of participants was good, hosts were great and incredibly kind, school material was good, challenge offered a great hands-on experience... Though, there was not enough time for attending the lectures, doing the challenge and exploring the city, so it would be good if the school lasted 7 instead of 4 days. Also, a small tour around the city center on the first day of the school would have been nice."
-- Ana Trisovic
"So I really liked the kaggle competition, it was a lot of fun and I really learned to apply things there. However, I do admit that it greatly distracted me from the seminar sessions and the lectures. I learn more from 'doing' rather than just listening however so it was okay for me but it would have been nice to have been more focused as well. I'm not sure how to balance things in such a short and intensive school."
-- Jared Vasquez
"Seminars were very demanding for me. I felt not like advanced, but more like intermediate level, so it took a significant effort of me to do the seminars. But as I wrote before, it was very useful. It is also great, that seminar's notebooks are avalaible on GitHub, so I can finish those I haven't done."
-- Dmitry Petrov
"It was an excellent contest. Thanks to it I learnt harder and tried to understand machine learning methods deeper. It really improved my practical skills, especially problem-solving ability."
-- Aibek Alanov
"The venue was very good but it would have been nice to be closer to the city center (nevertheless, I understand it was convenient for the school). Food can be improved and more time to work on the contest would be great.
In general, very good school, I am very happy I attended and I enjoyed and learnt a lot. Congratulations to all the organizers."
-- Antonio Romero
"I liked the high level quality of speakers and team working among participants."
-- Leonardo Cristella