Wydział Fizyki, Astronomii i Informatyki Stosowanej,
Uniwersytet Jagielloński w Krakowie
Rok
akademicki
2020/2021
| Date |
Topic |
Root/C++ or use PyRoot |
Datasets/Tutorials |
Python + Anaconda |
Datasets/Tutorials |
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13.10.2020 |
Getting organised with the framework Data exploration Introduction-labs |
Select
few examples from this link: histograms eg.: h1draw fibonacci filrandom ratioplot1 or from PyRoot examples from this link: PyRoot |
HowToStart |
assignement-0-python assignement-0-numpy assignement-0-numpy-matplotlib assignement-0-pandas |
HowToStart DS-cheatsheet_numpy.pdf DS_cheatsheet_matplotlib.pdf DS_cheatsheet_jupyter_notebook.pdf DS_cheatsheet_pandas.pdf numpy tutorial matrix algebra tutorial |
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| Statistics and Data
Analysis |
scripts in K. Cranmer, Statistics and Data Science |
PYHF: python based fitting/limit-setting/interval estimation |
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| 20.10.2020 | StatAnal_labs-lecture-1.txt StatAnal_labs-lecture-2.txt |
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| 27.10.2020 | StatAnal_labs-lecture-3.txt StatAnal_labs-lecture-4.txt |
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| 3.11-29.11.2020 |
StatAnal-project: select one from suggested topics or propose your own. OR Solve more theoretical/conceptual problems: link |
1)
Modeling
tools: RooFit, RooStats and HistFactory LHCStatAnal-labs-4-Root follow exercises there 2) Interval Estimation and Hypotheses Testing by T. Dorigo, IN2P3 School on Statistics, 2018 slides follow exercises there 3) Higgs signal at LHC by I. van Vulpen, Terascale Statistics School, DESY 2018 slides, exercises doc DesyCode2018.tgz follow exercises there 4) Roofit and Roostats by V. Verkerke, Terascale Statistics School, DESY 2011 slides, part I exercises, part I macros, part I slides, part II exercises, part II macros, partII follow exercises there |
1)
Bayesian inference Bayesian_inference.ipynb 2) Non-parametric inference Non_parametric inference.ipynb 3) Gaussian processes Gaussian_processes.ipynb monthly_in_situ_co2_mlo.csv 4) Try out PYHF tool on exercises proposed for Rootfit/Roostats PYHF: python based fitting/limit-setting/interval estimation |
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| Multivariate techniques and Machine Learning | |||||||
| 24.11.2020 | |||||||
| 1.12.2020 | |
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8.12.2020-10.01.2021 |
MVandML-project |
1)
BDTs and TMVA by I. Coadou, IN2P3 School on Statistics, 2018 slides Apply.C Train.C dataSchachbrett.root follow exercises there 2) Unfolding RooUnfold slides follow exercises there 3) Analysis of ATLAS open data with MV or ML methods 4) Analysis of ATLAS data optimising electron identification |
1) Principal Component
Analysis slides PCA_Clustering.ipynb 2) Unfolding.ipynb 3) Analysis of ATLAS open data BDT example for H->4l infofile.py 4) Unfolding with Gaussian processes https://github.com/adambozson/gp-unfold |
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| Physics Modeling, Simulation and Monte Carlo Methods | |||||||
| 15.12.2020 |
PhysModel_lab.txt |
Monte
Carlo methods by M. Chrzaszcz, ETH Zurich follow few exercises there |
MC.ipynb |
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| Regression, Classification, Clustering and Retrieval | |||||||
| 20.12.2020-26.01.2021 | Select
one topic, you may start from the scripts
linked, and build into a bigger project.
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Assignment_1_without_code.ipynb Assignment_2_without_code.ipynb kc_house_data.csv.zip, info on kc_house Assignment_4_without_code.ipynb amazon_baby.csv.zip Assignment_5_without_code.ipynb people_wiki.cvs.zip |
Other
datasets lending-club-data.csv.zip http://snap.stanford.ed/data/amazon/ http://mlr.cs.umass.edu/ml/datasets.html https//data.world/ |
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Ostatnia modyfikacja: 22 October 2020