TALENT SCHOOL "SPECIAL EDITION 2020"
Online Event - ECT*
Strada delle Tabarelle, 286
Trento - Italy
A 2020 “special edition” of the TALENT School on Machine learning will be held from 22 June 2020 to 03 July 2020 as a “remote” course. It is intended as a preparatory course for next year’s school in the usual setting at Villa Tambosi.
The course will be taught as an online intensive course of duration of two weeks, with a total time of 20 h of lectures and 10 h of exercises, questions and answers. Videos and digital learning material will be made available one week before the course begins. It is possible to work on a final assignment of 2 weeks of work. The total load will be approximately 80 hours, corresponding to 5 ECTS in Europe. The final assignment will be graded with marks A, B, C, D, E and failed for Master students and passed/not passed for PhD students. A course certificate will be issued for students requiring it from the University of Trento.
The organization of a typical course day is as follows:
Time and Activity
2pm-4pm (Central European time=CET) Lectures, project relevant information and directed exercises
5pm-6pm (CET) Questions and answers, Computational projects, exercises and hands-on sessions
Organizers
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Daniel Bazin (Michigan State University)
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Morten Hjorth-Jensen (Michigan State University)
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Michelle Kuchera (Davidson College)
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Sean Liddick (Michigan State University)
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Raghuram Ramanujan (Davidson College)
Student Coordination
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Morten Hjorth-Jensen (Michigan State University and University of Oslo)
Registration
Registration no longer available.
Secretariat
Barbara Gazzoli (ECT*)
Schedule
The lecture plan is as follows
Week 1
- Monday Linear Regression and intro to statistical data analysis (Morten Hjorth-Jensen MHJ). Learning slides at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Introduction/html/Introduction.html and https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day1/html/Day1.html and link to video from lecture June 22 https://mediaspace.msu.edu/media/t/1_ogq38oqq
- Tuesday Logistic Regression and classification problems, intro to gradient methods (MHJ). Learning slides at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day2/html/Day2.html and link to video for first lecture at https://mediaspace.msu.edu/media/t/1_po1a5e9v and second lecture at https://mediaspace.msu.edu/media/t/1_wbz4v2gm
- Wednesday Decision Trees, Random Forests and Boosting methods (MHJ). Learning slides at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day3/html/Day3.html and link to video at https://mediaspace.msu.edu/media/t/1_vrt5rxls
- Thursday Basics of Neural Networks and writing your own Neural Network code (MHJ). Learning slides at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day4/html/Day4.html and link to video at https://mediaspace.msu.edu/media/t/1_ksuz0ero. The link to the video of the additional exercise session is at https://mediaspace.msu.edu/media/t/1_shte4iw5
- Friday Beta-decay experiments, how to analyze various events, with hands-on examples . (Sean Liddick) Videos and teaching material https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day5/html/Day5.html. Link to video of online lecture at https://mediaspace.msu.edu/media/1_5n2bssbl. The link to the video of the additional exercise session is at https://mediaspace.msu.edu/media/1_q74f31cw
Week 2
- Monday Neural Networks and Deep Learning (Raghu Ramanujan, RR). PDF file of the presented slides at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day6/pdf/Day6.pdf. Jupter-Notebook at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day6/ipynb/Day6.ipynb. Video of lecture at https://mediaspace.msu.edu/media/t/1_58a9xrbt. Video of exercise session at https://mediaspace.msu.edu/media/t/1_ulont3rg
- Tuesday From Neural Networks to Convolutional Neural Networks and how to analyze experiment (classification of events and real data) (Michelle Kuchera, MK). Jupyter-notebook of lecture at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day7/ipynb/Day7.ipynb. Video of lecture https://mediaspace.msu.edu/media/t/1_2ysd5plh and video of exercise at https://mediaspace.msu.edu/media/t/1_watjxppf
- Wednesday Discussion of nuclear experiments and how to analyze data, presentation of simulated data from Active-Target Time-Projection Chamber (AT-TPC) (Daniel Bazin). Slides of lectures (PDF) at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day8/pdf/Day8.pdf. Videos and teaching material https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day8/html/Day8.html. Video of actual lecture at https://mediaspace.msu.edu/media/t/1_azaquoc0. Video of analysis of data with CNNs (MK) at https://mediaspace.msu.edu/media/t/1_rozywc7h. Jupyter-notebook of hands-on session at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day8/ipynb/Day8.ipynb
- Thursday Generative models (MK). Slides of lectures (PDF) at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day9/pdf/Day9.pdf. Jupyter-notebook at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day9/ipynb/Day9.ipynb. Video of lecture at https://mediaspace.msu.edu/media/t/1_ayfst99b. Video of exercise session at https://mediaspace.msu.edu/media/t/1_wpdmt7cw.
- Friday Reinforcement Learning (RR). Slides of lectures (PDF) at https://nucleartalent.github.io/MachineLearningECT/doc/pub/Day10/pdf/Day10.pdf. Future directions in machine learning and summary of course. Video of first lecture at https://mediaspace.msu.edu/media/t/1_0eiikln6. Video of second lecture at https://mediaspace.msu.edu/media/t/1_wzyabacr.