Syllabus for Application-Oriented Deep Learning in Physics

Tillämpad djupinlärning i fysik

Syllabus

  • 5 credits
  • Course code: 1FA368
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Physics A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2021-03-25
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2021
  • 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.

  • Responsible department: Department of Physics and Astronomy

Learning outcomes

On completion of the course the student shall be able to:

  • summarize the concepts of deep learning
  • apply deep learning for typical physics problems
  • design and optimize network architectures for different problems in physics and engineering

Content

Fundamentals of Deep Learning. Generalization, Regularization and Validation. Optimization and Hyperparameter Tuning; Convolutional Neutral Networks; Classification and Regression Tasks; Visualization & Advanced Computer Vision Methods; Autoencoders; Generative models, variational autoencoders, generative adversarial networks. Applications in different fields within physics, for example the design a trigger algorithm to measure cosmic neutrinos with a detector in Antarctica, and analyze data of the world's largest particle accelerator LHC. Analysis of big data sets in experimental physics. Astroparticle physics, elementary particle physics, image recognition, de-noising of data. Practical skills of using the TensorFlow framework via the high-level Keras python interface.

Instruction

Lectures and exercise classes.

Assessment

Hand-in problems.

Reading list

Reading list

Applies from: Autumn 2021

Some titles may be available electronically through the University library.

  • Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron Deep learning

    Cambridge, MA: MIT Press, [2016]

    Find in the library

    Mandatory

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