Please note that this is NOT a compulsory reading list!
The purpose of this reading list is to provide a good range of material for some of the areas covered in the CSC lectures. It aims to provide some pointers should you wish you explore some areas a little before you come to the School, or if you are interested in a topic following the School and wish to read more about it.
Due to the varying backgrounds of participants, some will be more familiar with certain areas than others, and we certainly do not anticipate that students will explore each area in equal depth.
Lecture series: Software security
- Secrets and Lies: Digital Security in a Networked World
- Bruce Schneier
- Security Engineering: A Guide to Building Dependable Distributed Systems
- Ross Anderson
- Writing Secure Code
- Michael Howard, David LeBlanc
- Secure Coding: Principles and Practices
- Mark G. Graff, Kenneth R. van Wyk
Lecture series: Computer Architecture and Performance Tuning
Lecture series: Networking performance
- Computer Networks, Ed. 4
- Andrew Tannenbaum, Prentice Hall, ISBN 0-130-661023
- Internetworking with TCP/IP, vol 1
- Douglas E. Commer, Prentice Hall, ISBN 0-130-183806
- Understanding Networked Multimedia
- Francois Fluckiger, Prentice Hall, ISBN 0-131-90992-4
Lecture series: Software Design in the Many-Cores Era
- C++ Concurrency in Action Practical Multithreading
- Anthony Williams, February 2012
- ISBN: 9781933988771
- Intel Threading Building Blocks: Outfitting C++ for Multi-Core Processor Parallelism
- Learning Spark: Lightning-Fast Big Data Analysis.
- Matei Zaharia, Patrick Wendell, Andy Konwinski, Holden Karau, O'Reilly Media
- ISBN: 9781449359034
Lecture series: Introduction to Physics Computing
- Quirky Quarks: A Cartoon Guide to the Fascinating Realm of Physics
- Benjamin Bahr, Springer; 1st ed. 2016 edition, ISBN-10: 3662495074
Interesting also for physicists:
- The Large Hadron Collider: The Extraordinary Story of the Higgs Boson and Other Stuff That Will Blow Your Mind
- Donald Lincoln, Johns Hopkins University Press (21. August 2014), ISBN-10: 1421413515
Lecture series: Tools & Techniques for Physics Computing
There is a wealth of material at Prof. Jacobsen's Tools & Techniques bibliography.
The content listed is more designed to "learn more after the School", but is a useful reference should you wish to delve into some of the topics beforehand.
Lecture series: Data analysis
Spend few minutes to familiarize yourself with following concepts:
- Data Analysis https://en.wikipedia.org/wiki/Data_analysis
- Monte Carlo method https://en.wikipedia.org/wiki/Monte_Carlo_method
- Least squares fitting https://en.wikipedia.org/wiki/Least_squares
- Experimental errors https://en.wikipedia.org/wiki/Observational_error
- Statistical Data Analysis.
- G. Cowan, Oxford University Press, 1998.
- Bayesian Reasoning in High-Energy Physics: Principles and Applications.
- G. D’Agostini, Technical report, CERN-99-03, 1999.
- Geant4 exercises at CERN School of Computing.
- A. Heikkinen and M. Liendl, Talk given at 13th Geant4 Collaboration Workshop with related exercises, and ah08gCode.tar.gz, 65 MB, October 11th 2008.
- TMVA - Toolkit for Multivariate Data Analysis.
- A. Hocker, CERN-OPEN-2007-007, 2007.
- Data Analysis with ROOT C /
- A. Heikkinen, [arXiv: physics/0703039].
- Workshop on Confidence Limits.
- F. James, Technical report, CERN-2000-005, 2000.
- Statistics for nuclear and particle physicists.
- L. Lyons, Cambridge University Press, 1992.
- ROOT 5.21 Users Guide, October 2008.
- Data Analysis: a Bayesian Tutorial.
- D. S. Sivia, Oxford University Press, 2000
Lecture series: Multivariate Analysis
- Pattern Recognition and Machine Learning by C. M. Bishop
- Data Mining: Concepts and Techniques by J. Han, M. Kamber, J. Pei
- Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods by O. Behnke, K. Kröninger, G. Scott, T. Schörner-Sadenius
- Deep Learning (Adaptive Computation and Machine Learning) by I. Goodfellow, Y. Bengio, A. Courville
- Statistische und numerische Methoden der Datenanalyse by V. Blobel (www.desy.de/~blobel/eBuch.pdf)
- Stochastic gradient boosting by J. H. Friedman
- Deep Learning by Y. LeCunn, Y. Bengio, G. Hinton
- Representation Learning: A Review and New Perspectives by. Y. Bengio, A. Courville, P. Vincent
- "Why does deep and cheap learning work so well?" by H. W. Lin, M. Tegmark