Introduction to Deep Learning - NUPA032

• Title: Introduction to Deep Learning
• Guaranteed by: University of Passau (32-PASSAU)
• Faculty: Faculty of Mathematics and Physics
• Actual: from 2025
• Semester: winter
• E-Credits: 6
• Hours per week, examination: winter s.:2/2, Ex [HT]
• Capacity: unlimited
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Guarantor: prof. Stefanie Scherzinger, Ph.D.
• Teacher(s): prof. Stefanie Scherzinger, Ph.D.
• Incompatibility : NPFL138
• Interchangeability : NPFL138

Annotation

University of Passau - Code: 471616; Lecturer: Lemmerich; Course content: Basics of Representation Learning. Perceptron Learning. Feedforward Neural Networks. Gradient Descent and Backpropagation. Regularization in Deep Learning. Convolutional Neural Networks. Recurrent Neural Networks. Autoencoders. Adversarial Training. Graph Neural Networks. Applications of Deep Learning for Text, Sequences, and Images. Explainability and Deep Learning.

Last update: Holubová Irena, doc. RNDr., Ph.D. (06.03.2025)

Literature

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville: Deep learning. MIT press, 2016;

Aggarwal, Charu C.: Neural networks and deep learning. Springer 10 (2018): 978-3;

Additional literature will be announced at the beginning of the semester.

Last update: Holubová Irena, doc. RNDr., Ph.D. (06.03.2025)