Deep Learning 2

Data is displayed for academic year: 2023./2024.

Laboratory exercises

Course Description

The idea of generating new but convincing samples has always been interesting in the fields of artificial intelligence and machine learning and is generally considered a more difficult problem than the problems solved by discriminative models. Recent advances in specialized deep model architectures, combined with advances in optimization methods, have enabled the successful modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study mathematical foundations and learning algorithms for deep generative models, including popular families of generative models such as variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow models, and energy function-based models. Through practical laboratory exercises, the design, training and testing of some of the most famous deep generative models will be experienced.

Study Programmes

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Learning Outcomes

  1. Define main notions of deep generative models.
  2. Explain the underlying technology of deep generative models.
  3. Describe the differences between the most prominent deep generative models.
  4. Evaluate different methods for sampling from an unknown probability distribution.
  5. Identify scenarios where deep generative models can be applied.

Forms of Teaching


The lectures present theoretical concepts and algorithms followed by concrete examples.


Laboratory exercises are organized as projects.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 20 %
Seminar/Project 20 % 20 % 20 % 20 %
Mid Term Exam: Written 20 % 30 % 0 %
Final Exam: Written 20 % 30 %
Exam: Written 50 % 60 %

The threshold on the sum of the midterm and the final exam is 50%.

Week by Week Schedule

  1. Course overview, introduction to deep generative learning.
  2. Mathematical Foundation & Basic Concept for generative learning.
  3. Basic concepts of deep learning, deep models and unsupervised learning.
  4. Traditional approaches to generative modeling.
  5. Autoregressive Models.
  6. Classical autoencoders.
  7. Variational autoencoders.
  8. Midterm.
  9. Advanced variational autoencoders.
  10. Normalising flow models.
  11. Generative adversarial networks.
  12. Advanced generative adversarial networks.
  13. Energy based models.
  14. Deep energy based models.
  15. Final exam.


Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016.), Deep Learning, MIT Press
Eli Stevens, Luca Antiga, and Thomas Viehmann (2020.), Deep Learning with PyTorch, Manning Publications

For students


ID 230255
  Winter semester
L0 English Level
L2 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

87 Excellent
75 Very Good
63 Good
51 Sufficient