# Mathematics 1,280 courses and talks. - [Escaping the echo chamber](https://invisiblenetwork.ai/learn/cern/3009899): Patrick Koppenburg · CERN - [Outreach: Just do it](https://invisiblenetwork.ai/learn/cern/3010095): Sascha Mehlhase · CERN - [Abolish Outreach!](https://invisiblenetwork.ai/learn/cern/3009398): James Beacham · CERN - [Physics Outreach: Why? & How?](https://invisiblenetwork.ai/learn/cern/3009903): Steven Goldfarb · CERN - [Just for the kick of it - learning by playing](https://invisiblenetwork.ai/learn/cern/3009400): Joao Antunes Pequenao · CERN - [The Art and Science initiative](https://invisiblenetwork.ai/learn/cern/3008667): Dario Menasce · CERN - [Science divulgation at a literature festival](https://invisiblenetwork.ai/learn/cern/3009898): Matteo Polettini · CERN - [Engaging formats for science events](https://invisiblenetwork.ai/learn/cern/3011356): Leonardo Alfonsi · CERN - [Measuring the impact of the Higgs](https://invisiblenetwork.ai/learn/cern/3009521): Jonathan Butterworth · CERN - [Our Universe in a Box](https://invisiblenetwork.ai/learn/cern/3009518): Claire Lee · CERN - [Panel session and conclusions](https://invisiblenetwork.ai/learn/cern/3009500): Tommaso Dorigo and Sergei Gleyzer · CERN - [Introduction to Statistics I](https://invisiblenetwork.ai/learn/cern/3009356): Glen Cowan · CERN - [Introduction to Machine Learning I](https://invisiblenetwork.ai/learn/cern/3008013): Sergei Gleyzer · CERN - [Introduction to ROOT withs hands-on session](https://invisiblenetwork.ai/learn/cern/3009392): Danilo Piparo · CERN - [Introduction to C++](https://invisiblenetwork.ai/learn/cern/3011031): Wouter Verkerke · CERN - [Introduction to Python](https://invisiblenetwork.ai/learn/cern/3010726): Graeme Stewart · CERN - [Adatelemzés és számítástechnika / Data Analysis and Computing](https://invisiblenetwork.ai/learn/cern/3007912): Attila Krasznahorkay · CERN - [SG5 // Data analysis in particle physics](https://invisiblenetwork.ai/learn/cern/3007386): Özhan Fenerci and others · CERN - [Improving ABCD Analysis in the Single Vertex LLP Search](https://invisiblenetwork.ai/learn/cern/3007241): David Pierce Aguillard · CERN - [Multivariate analysis for the $W\rightarrow\pi+\gamma$ search](https://invisiblenetwork.ai/learn/cern/3007372): Jeske Dioquino · CERN - [Compton Scattering as Antiproton Beam Diagnostics Tool, C2^− Fluorescence, and Positronium Data Analysis](https://invisiblenetwork.ai/learn/cern/3014445): Patrick Michael Brown · CERN - [Kinematic Analysis of TrackML Challenge Submissions](https://invisiblenetwork.ai/learn/cern/3007240): Emily Ashlynne Ramey · CERN - [Malware Analysis Management](https://invisiblenetwork.ai/learn/cern/3006854): Shivam Kapoor · CERN - [Apache Spark for Scalable Physics Analysis](https://invisiblenetwork.ai/learn/cern/3019232): Vasileios Dimakopoulos · CERN - [Quantitative Analysis of data Caching for the HL-LHC DataLake](https://invisiblenetwork.ai/learn/cern/3006573): Irvin Jadurier Umana Chacon · CERN - [ATLAS Open Data: software for visualisation and physics analysis](https://invisiblenetwork.ai/learn/cern/3006476): Ya-Feng Lo · CERN - [Hadronic Vertex Analysis](https://invisiblenetwork.ai/learn/cern/3006497): Natasha Mahmood · CERN - [Analysis of the TOF single-channel performance by using cosmic rays triggered in the ALICE experiment during the 2016/17 data taking campaign](https://invisiblenetwork.ai/learn/cern/3006547): Zuzana Moravcova · CERN - [Multivariate analysis techniques for neutral pileup suppression in Particle Flow](https://invisiblenetwork.ai/learn/cern/3006491): Melina Luthi · CERN - [From Collision to Discovery: Physics Analysis with Apache Spark](https://invisiblenetwork.ai/learn/cern/3006472): Vaggelis Motesnitsalis · CERN - [Foundation of Statistics (4/4)](https://invisiblenetwork.ai/learn/cern/3003317): Andreas Hoecker · CERN - [Foundation of Statistics (3/4)](https://invisiblenetwork.ai/learn/cern/3003730): Andreas Hoecker · CERN - [Foundation of Statistics (2/4)](https://invisiblenetwork.ai/learn/cern/3002997): Andreas Hoecker · CERN - [Foundation of Statistics (1/4)](https://invisiblenetwork.ai/learn/cern/3002962): Andreas Hoecker · CERN - [Interactive Data Analysis for End Users](https://invisiblenetwork.ai/learn/cern/3001376): Enrico Bocchi · CERN - [Statistics for High Energy Physics](https://invisiblenetwork.ai/learn/cern/3020170): Eilam Gross · CERN - [Statistics for High Energy Physics](https://invisiblenetwork.ai/learn/cern/3012635): Eilam Gross · CERN - [Statistics for High Energy Physics](https://invisiblenetwork.ai/learn/cern/3004343): Eilam Gross · CERN - [Hyy Couplings Background Analysis](https://invisiblenetwork.ai/learn/cern/3010106): Noah Alexander Zipper · CERN - [Heterotic Geometry and Effective Theories - 4](https://invisiblenetwork.ai/learn/cern/3005897): Lara Anderson and James Gray · CERN - [Heterotic Geometry and Effective Theories - 3](https://invisiblenetwork.ai/learn/cern/3005748): Lara Anderson and James Gray · CERN - [Heterotic Geometry and Effective Theories - 2](https://invisiblenetwork.ai/learn/cern/3005754): Lara Anderson and James Gray · CERN - [Heterotic Geometry and Effective Theories - 1](https://invisiblenetwork.ai/learn/cern/3005635): Lara Anderson and James Gray · CERN - [Mathematics, Cryptography, Blockchains, and Cryptocurrencies. Myths and realities.](https://invisiblenetwork.ai/learn/cern/3016449): Alberto Pace · CERN - [Optimizing Astrophysical Simulation and Data Analysis codes on Intel Architectures](https://invisiblenetwork.ai/learn/cern/3002045): Salvatore Cielo · CERN - [Performance and Scalability Analysis of CNN-based Deep Learning Inference in the Intel Distribution of OpenVINO Toolkit](https://invisiblenetwork.ai/learn/cern/3019005): Iosif Meyerov · CERN - [“Big Data In HEP” - Physics Data Analysis, Machine learning and Data Reduction at Scale with Apache Spark](https://invisiblenetwork.ai/learn/cern/3002562): Luca Canali · CERN - [Adatelemzés és számítástechnika / Data Analysis and Computing](https://invisiblenetwork.ai/learn/cern/2302785): Attila Krasznahorkay · CERN [Next page](https://invisiblenetwork.ai/learn/browse?subject=maths&page=23)