Teoretyczne Podstawy Informatyki

Advanced Methods for Data Analysis

Wydział Fizyki, Astronomii i Informatyki Stosowanej,

Uniwersytet Jagielloński w Krakowie

Rok akademicki 2026/2027

Konsultacje: sroda, godz. 8:00 - 10:00; pokój G-0-10.

Exam: 28.01.2027, 9am-11am, A-1-03
            29.01.2027, 9am-11am, A-1-03

Lectures
---------
Recommended books/articles for reading:

=> G. Cowan, "Statistical Data Analysis"
      https://www.pp.rhul.ac.uk/~cowan/stat_course.html
      recorded lectures at CERN Summer School 2023: link
                                                                         2025: link
=> F. James, "Statistical Methods in Experimental Physics"
=> J. Narsky, F. Porter, "Statistical Analysis Techniques in Particle Physics"
=> J. A.Rice, "Mathematical Statistics and Data Analysis"
=> I. Narsky, F. C. Porter, "Statistical Analysis Techniques in Particle Physics"
=> K. Cranmer, "Practical statistics for LHC"
=> A. Segura and A. Catalin-Parra, "A Practical Guide to Statistical Techniques in Particle Physics"
      https://arxiv.org/pdf/2411.00706

Recent conferences and workshops:

=> Terascale Statistics School 2026

=> PHYSTAT (2026): Statistics meets Machine Learning

=> PHYSTAT Workshops and Schools

Date	Lecture slides	Additional material
	Statistics and Data Analysis: Basics
6.10.2026	Introduction StatAnal-lecture-1
13.10.2026	StatAnal-lecture-2
20.10.2026	StatAnal-lecture-3
27.10.2026	StatAnal-lecture-4
	Statistics and Data Analysis: Advanced	Statistical methods for LHC (advanced)
3.11.2026
10.11.2026	LHCStatAnal-lecture-1	https://arxiv.org/pdf/1007.1727.pdf https://arxiv.org/pdf/1609.04150.pdf https://arxiv.org/pdf/1807.05996.pdf https://arxiv.org/pdf/2101.06944.pdf HistFactory Pyhf https://arxiv.org/pdf/2109.04981.pdf
17.11.2026	LHCStatAnal-lecture-2
24.11.2026	LHCStatAnal-lecture-3
	Statistics and Data Analysis: Questions for exam
	BELOW NOT UPDATED YET	BELOW NOT UPDATED YET
	Multivariate Techniques and Machine Learning
1.12.2026	Unfolding-lecture	https://arxiv.org/pdf/1910.14654.pdf F. Spano-Proc14-02/P52.pdf https://arxiv.org/pdf/1611.01927.pdf
8.12.2026	MVandML-lecture-1	https://arxiv.org/pdf/1506.02169.pdf P.Bhat, Multivariate_Analysis_Methods_in_Particle_Physics Understanding Deep Learning https://arxiv.org/pdf/1806.11484.pdf ML4Jets 2024 workshop https://atlas.cern/Updates/Feature/Machine-Learning
15.12.2026	MVandML-lecture-2	https://iopscience.iop.org/article/ 10.1088/1748-0221/11/01/P01019/pdf https://arxiv.org/pdf/1812.09722.pdf ATL-PHYS-PUB-2019-033.pdf ATL-PHYS-PUB-2020-018.pdf GNN in ATLAS flavour tagging
	MV and ML: Questions for exam

	Physics Modeling, Simulation and Monte Carlo Methods
12.01.2027	PhysModelandSimul-lecture	https://www.coursera.org/learn/ modeling-simulation-natural-processes The XII Gean4 International School 2024
19.01.2027	MCandGenerators-lecture	Introduction to MC methods for HEP https://rivet.hepforge.org/
26.01.2027	Simulation based inference	https://arxiv.org/pdf/1911.01429 https://arxiv.org/pdf/2010.06439

1st PhyStat School of Statistics (2025): Statistics in the era of ML
https://indico.cern.ch/event/1537633/timetable/#all

Aachen Online Statistics School 2023
https://indico.desy.de/event/37562/timetable/

Statistical Analysis in HEP Physics
N. Berger, Foundation of Statistics, Lectures at CERN Summer School 2019
link1, link2, link3

Statistics and Data Science
K. Cranmer, Course at NYU Physics, Fall 2020, link

Machine learning applications in HEP physics:

B. Nachman,
"Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics"

M. Stoye,
"ML applications in CMS"

ML techniques in HEP, Workshop, Berkeley Laboratory, 11 - 13 December 2018
https://indico.physics.lbl.gov/indico/event/546/

A. Castaneda, LHCP conference, Puebla, Mexico, 2019
ML and Big data tools at HEP,

Last part of the course will be based on the materials from Coursera:

Dr. Mine Çetinkaya-Rundel "Data Analysis and Statistical Inference"
C. Guestrin and E. Fox, "Machine Learning Specialisation"
        Foundation: link
        Regression: link
        Classification: link
        Clustering and Retrieval: link

Related interesting material from Coursera
D. Peng, J. Leek and B. Caffo, " Exploratory Data Analysis"
J. Leskovec, A. Rajaraman and J. Ullman, "Mining Massive Datasets"
B. Caffo, R. D. Peng and J. Leek, "Regression Models"
B. Chopard et al., "Simulation and modeling of natural processes"

Collection of datasets
http://mlr.cs.umass.edu/ml/datasets.html
http://faculty.marshall.usc.edu/gareth-james/ISL/data.html
http://snap.stanford.edu/data/amazon/

Useful links:
https://turi.com/download/install-graphlab-create-aws-coursera.html
https://turi.com/download/academic.html
https://github.com/turi-code/SFrame

Clustering
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html

Boosting
https://turi.com/learn/userguide/supervised-learning/boosted_trees_classifier.html
https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
-----------------
[1] https://class.coursera.org/statistics
[2] http://www.openintro.org/stat/textbook.php
[3] https://class.coursera.org/exdata-006
[4] https://class.coursera.org/mmds
[5] http://www.mmds.org/
[6] http://www.cs.cmu.edu/~awm/tutorials.html

Additional materials
http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
http://statweb.stanford.edu/~tibs/ElemStatLearn/
http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set
http://www.youtube.com/watch?v=wQhVWUcXM0A

Ostatnia modyfikacja: 6 October 2026

Elzbieta Richter-Was

Wstecz