Data Science in Physics
1
2020-2021
02038668
Optional
Portuguese
English
Face-to-face
SEMESTRIAL
6.0
Elective
2nd Cycle Studies - Mestrado
Recommended Prerequisites
Advanced knowledge of statistics and programming.
Teaching Methods
Classes will be mostly practical using:
- experimental data from Particle Physics;
- computational tools for ab-initio Condensed Matter Physics simulations.
Learning Outcomes
- Understand the statistical data analysis techniques from the point of view of Particle Physics.
- Understand how to deal with systematic errors and how to establish criteria for identification of a discovery in Particle Physics.
- Understand the process of determination of experimental sensitivity.
- Use data science computational tools to reconstruct object such as, e.g., hadronic jets.
- Use Monte Carlo methods in Particle Physics data analysis.
- Understand the use of machine learning tools in Condensed Matter Physics.
- Understand the classifiers used in Condensed Matter Physics.
- Analyse, synthesise and process information.
- Prepare, process, interpret and communicate physical information, using relevant bibliographical sources, an appropriate speech and the right tools in classroom.
Work Placement(s)
NoSyllabus
Introduction.
Review of some topics in data analysis.
Experimental measurements and criteria to establish a discovery.
Determination of the experimental sensitivity.
Systematic errors.
Computational tools for data analysis.
Practical applications using experimental data from Particle Physics.
Machine learning in Condensed Matter Physics: materials' design and speeding-up of ab-initio methods.
Head Lecturer(s)
José Ricardo Morais Silva Gonçalo
Assessment Methods
Assessment
Resolution Problems: 40.0%
Project: 60.0%
Bibliography
Glen Cowan, Statistical Data Analysis, Oxford University Press, 1998
Ilya Narsky and Frank C. Porter, Statistical Analysis Techniques in Particle Physics, Wiley, 2014
Luca Lista, Statistical Methods for Data Analysis in Particle Physics, Springer, 2017
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
A. R. Oganov, ed., Modern methods of crystal structure prediction, Wiley, 2010
T. Lookman, S. Eidenbenz, F. Alexander, and C. Barnes, eds., Materials Discovery and Design By Means of Data Science and Optimal Learning, Springer, 2018
Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, and Miguel A. L. Marques, Recent advances and applications of machine learning in solid-state materials science, Psi_k Scientific Highlight Of The Month, March 2019