Application-Oriented Deep Learning in Physics, 5 credits
Academic year 2022/2023
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Autumn 2022, 17%, Campus
Start date: 29 August 2022
End date: 15 January 2023
Application deadline: 19 April 2022
Application code: UU-13049 Application
Language of instruction: English
Location: Uppsala
Selection: Higher education credits in science and engineering (maximum 240 credits)
Registration: 28 July 2022 – 28 August 2022
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Autumn 2022, 17%, Distance learning
Start date: 29 August 2022
End date: 15 January 2023
Application deadline: 19 April 2022
Application code: UU-13116 Application
Language of instruction: English
Location: Flexible
Selection: Higher education credits in science and engineering (maximum 240 credits)
Outline for distance course: Communication between teachers and students is done using the learning management system and e-meeting tools. A computer with a stable internet connection and webcam is required for participating in the course and examination.
Number of mandatory meetings on campus: 0
Number of voluntary meetings on campus: 0
Registration: 28 July 2022 – 28 August 2022
Entry requirements: 120 credits in science/engineering with Computer Programming I, Quantum Physics or Solid State Physics I/Statistical Mechanics. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Fees:
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application or tuition fees. Formal exchange students will be exempted from tuition fees, as well as the application fee. Read more about fees.
Application fee: SEK 900
Tuition fee, first semester: SEK 12,083
Tuition fee, total: SEK 12,083
About the course
Machine learning has made tremendous progress over the last years. The advances in computing resources together with the development of algorithms to efficiently train deep neural networks - deep learning - results in an unprecedented performance, for example in image recognition. Applying deep learning to physics problems is already revolutionising the field and improves the knowledge of our Universe and what matter is made of.
This course will introduce you to this cutting-edge technology and will equip you to apply deep learning. The course is practice oriented and we will use real-world examples from different areas of physics. You will, for example, design a trigger algorithm to measure cosmic neutrinos with a detector in Antarctica, and analyse data from the world's largest particle accelerator LHC. After the course you will have applied deep learning to a variety of physics problems and will be able to use deep learning in future projects.