Teaching Resources: This page is maintained by Prof. Adrian Bevan
This web page lists my teaching material (notes / materials / examples).
Machine Learning
- TensorFlow Tutorial: (TensorFlow-Tutorial (github), binder page), and the pdf file with suggested exercises.
- Introduction to Machine Learning: (indico page) [2020, RAL Particle Physics Division, UK]. (QMUL Indico Mirror)
- GRADnet Machine Learning and AI workshop: (html) [2020; QMUL, UK]
- Practical Machine Learning Summer School: (html) [2019; QMUL, UK]
- ATLAS UK Machine Learning Tutorial: (html) [2019, QMUL]
- Machine Learning in High Energy Physics: (html) [2018; CINVISTAV, Mexico]
- Practical Machine Learning Summer School: (html) [2018; QMUL, UK]
- Machine Learning in High Energy Physics: (pdf) [HORSE 2017; QMUL, UK]
- BaBar Analysis School: (html) [2009, 2011; SLAC, USA]
- Yeti School on Statistical Methods (html) [2007; IPPP, Durham, UK]
Graduate lectures
In addition to the machine learning related courses noted above I have given graduate lectures on the following topics
- Semiconductor Detectors: (pdf)
- Tracking Detectors: (pdf)
- Wire bonding: (pdf)
- Wire fusing: (pdf)
- Flavour Physics (IDPASC Summer School in Valencia, Spain)
- B Physics (Helmholtz Summer School, Dubna, Russia)
- CP violation (as part of the University of London graduate lecture programme for particle physics students)
- Unix and ROOT (as part of the University of London graduate lectureprogramme for particle physics students)
Undergaduate teaching
I currently teach the following undergraduate modules
- Practical Machine Learning (SUM401N)
- Statistical Data Analysis (SPA6328)
I have taught the following undergraduate modules
- Mathematical Techniques 1 (PHY121 / SPA4121)
- Quantum Mechanics B (PHY413 / SPA6413)
- Scientific Measurements (PHY103 / SPA4103)
Back to the home-page