mit-physics-data / lectures

Mit Data Science and Physics

This github contains the lecture materiasl for the data science and physics class.

The topics of each week's lectures are described in the syllabus on Canvas. Lectures are in directories labeled by lecture number. Additional data relevant to the lecture is availble from the lecture materials. Problem sets are on canvas, and on a separate github given by https://github.com/mit-physics-data/psets/ .

Grading and problem sets are due on canvas. Solutions to the problem sets will be availble instantaneously.

You should also review in-class notebooks and homework solutions to make sure you understand what is happening. The lecture notebooks have in-class exercises, not all will be covered in class.

Projects are availble on github at : https://github.com/mit-physics-data/projrects/

They will be posted in a timely manner before they are due.

Related Material:

MITx course: https://github.com/mitx-8s50/nb_LEARNER

UIUC Data Analyis and machine learning : https://illinois-mla.github.io/syllabus/

UCSD Data Science Capstone: https://dsc-capstone.github.io

CMS Collaboration, “2020 CMS Data Analysis School": https://lpc.fnal.gov/programs/schools-workshops/cmsdas.shtml

2020 Hands-on Advanced Tutorial Sessions at the LPC: https://lpc.fnal.gov/programs/schools-workshops/hats.shtml

Computational and data science training for high energy physics.: https://codas-hep.org

2021 Machine Learning and the Physical Sciences Workshop.: https://ml4physicalsciences.github.io/2021 P. Calafiura, D. Rousseau and K. Terao, Artificial Intelligence for High Energy Physics, World Scientific (2022), 10.1142/12200
UCSD “Particle Physics and Machine Learning.” https://jduarte.physics.ucsd.edu/capstone-particle-physics-domain 10.5281/zenodo.4768815

G. Cowan, “Statistics for Particle Physicists.” https://cds.cern.ch/record/2773595

The 2020 US-ATLAS Computing Bootcamp website : https://indico.cern.ch/event/933434

BU “Machine Learning for Physicists.” : http://physics.bu.edu/~pankajm/PY895-ML.html

UMN “Big Data in Astrophysics.” : https://github.com/mcoughlin/ast8581_2022_Spring

UIUC Fundamentals of Data science: https://github.com/gnarayan/ast596_2020_Spring

Vanderbilt Astrostatistics: https://github.com/VanderbiltAstronomy/astr_8070_s21

Drexel Big Data Physics: Methods of Machine Learning: https://github.com/gtrichards/PHYS_440_540

Caltech Astroinformatics: https://www.astro.caltech.edu/ay119/

GROWTH summer school: http://growth.caltech.edu/growth-school-2019.html

AURA winter school: http://www.aura-o.aura-astronomy.org/winter_school/ - go to Past Years.

YouTube Neural Networks: https://www.youtube.com/watch?v=aircAruvnKk