Entry requirements

120 credits in science/engineering with Introduction to Scientific Computing (F) and Linear Algebra II. Participation in Quantum Physics/Quantum Physics F. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• summarize the concepts of deep learning
• apply deep learning to typical problems in physics and engineering
• design and optimize network architectures for different problems in physics and engineering
• verify results from deep learning models with experimental data

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. Applications in physics and engineering, for example, image recognition, analysis of time series data, pulse shape discrimination, real-time low-power on-device computing (internet of things applications); Practical skills of using the TensorFlow framework via the high-level Keras python interface; Methods to verify neural network predictions, e.g., through independent experimental data that is obtained in a lab assignment.

Instruction

Lectures, exercise classes and laboratory.

Assessment

Hand-in problems.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.