ENGBMT 75027

Introduction to Deep Learning

The aim of the course is to acquire basic knowledge on deep learning. This includes classical neural network models and recent deep architectures. Topics such as convolutional neural networks, optimisation, regularisation, generative models, sequential models will be covered among others. In the exercise, the participants will implement some of the standard models for classification or regression, transfer learning, generative models and acquire knowledge on machine learning applications.

Useful Literature

  •     Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. www.deeplearningbook.org

  •     Rojas, R. (2013). Neural networks: a systematic introduction.

  •     Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning.


Dates, locations

Summer 2022


Monday 12:00−14:00
Room 43.2.104


Monday 14:00−16:00
Room 47.2.101






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