Dates, locations
Summer 2022
Lectures
Monday 12:00−14:00
Room 43.2.104
Exercises
Monday 14:00−16:00
Room 47.2.101
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.
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.
Summer 2022
Monday 12:00−14:00
Room 43.2.104
Monday 14:00−16:00
Room 47.2.101
5 ECTS
English
Folgen sie uns auf twitter!
@medtech_uulm
The information displayed on this page is for general information only and may not be complete. For legal binding information, please consult the currently active Modulhandbuch/FSPO of the respective study program. Day-to-day information is provided through the moodle page of the respective course (registration required).