Παρουσίαση/Προβολή

(INF549) -  ΘΕΜΟΣ ΣΤΑΦΥΛΑΚΗΣ

Περιγραφή Μαθήματος

Course content

Deep learning algorithms and models that allow computers learn from composite data. Deep convolutional neural networks, recurrent neural networks, stochastic training algorithms for large scale data. Unsupervised deep learning using variational auto-encoders,  generative adversarial networks and diffusion. Deep reinforcement learning with applications to robotics and for playing games.

Prerequisite Knowledge

Students should have basic knowledge of calculus, linear algebra and probability theory. For the programming assignments of the course students should have programming experience (e.g., in Python).

Learning Outcomes

After successfully completing the course, students will be able to:

• Describe basic concepts of deep learning.
• Describe a wide range of deep learning techniques (architectures, algorithms).
• Design and implement deep neural networks for a large number of machine learning problems.
• Evaluate the effectiveness and performance of deep neural networks.

Bibliography

• Deep Learning (Adaptive Computation and Machine Learning), Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016, ISBN-13: 978-0262035613
• Understanding Deep Learning, Simon J.D. Prince, The MIT Press, 2023.
• Deep learning: Foundations and concepts, Christopher M. Bishop , Hugh Bishop, Springer, 2024

Teaching methods

One 3-hour lecture per week

Assessment Criteria

The grade is based on the grades of the assignments.

Ημερομηνία δημιουργίας

Δευτέρα, 5 Οκτωβρίου 2026