Applied Deep Learning in Physics and Engineering

Education facts

Code: 1FA370

Fundamentals of Deep Learning. Generalization, Regularization and Validation, Optimization and Hyperparameter Tuning, Convolutional Neutral Networks, Recurrent Neural Networks, and Graph Neural Networks. Classification and Regression Tasks. Visualization & Advanced Computer Vision Methods. Autoencoders. Generative models, variational autoencoders, generative adversarial networks. Applications in physics and engineering, for example, image recognition, analysis of time series data, pulse shape discrimination, real-time low-power on-device computing (IoT 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. **Outline for distance course:** In the 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.

Entry requirements

120 credits in science/engineering. Linear Algebra II. Introduction to Scientific Computing or Introduction to Scientific Computing F. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Applied Deep Learning in Physics and Engineering

  Uppsala University

  Start date: 2 November 2026

  End date: 17 January 2027

  Pace of study: 33 %