LFU:online

The subject of this course is to gain deep knowledge in Deep Learning by studying the most state-of-the-art methods. We will build on the subjects that were presented in 703134 VU Deep Learning and study methods like Transformers, Multimodal Large Language Models (MLLMs) and Generative models.

The course will cover first a quick review on Deep Learning to shortly deepen into the most recent advances in Deep Learning: Transformers, Multimodality, Multimodal Large Language models (MLLMs), Image generation, Variational Autoencoders, Generative Adversarial Networks and Diffusion models.

Motivated by practical applications, the lecture covers theoretical material that the complementary proseminar exercises in discussions, written problems, and programming projects.

Written final exam and assignments/projects during the course.

1. Deep Learning: Foundations and Concepts (2023) by Christopher Bishop. Springer. ISBN-10: 3031454677, ISBN-13: 978-3031454677
2. Understanding Deep Learning (2024) by Simon Prince. MIT Press. ISBN-10: 0262048647. ISBN-13: 978-0262048644
   

It is strongly recommended to have passed 703134 VU Deep Learning.

Allocation of places in courses with a limited number of participants (PS, SE, VU, PJ)

In courses with a limited number of participants, course places are allocated as follows:

1. Students for whom the study duration would be extended due to the postponement are to be given priority.

2. If the criteria in no. 1 do not suffice, first, students for whom this course is part of a compulsory module are to be given priority, and second, students for whom this course is part of an elective module.

3. If the criteria in no. 1 and 2 do not suffice, the available places are drawn by random.

Curriculum MA Computer Science 2021W