- Course Structure
- Lecture Schedule
- Labs
- Project
- Updates
- Communication
- [17.02.2026] Assignment 2 deadline is extended to 22.02.2026 23.59
- [28.01.2026] Assignment 2 is now released, you should have received GitHub org invite and link to the assignment in Canvas. See this guide to get started and for useful tips.
- [23.01.2026] Early dialog and feedback is ready in Canvas (see annoucements).
- Assignment 1 is now released, you should have received GitHub org invite and link to the assignment in Canvas. See this guide to get started and for useful tips.
- Lectures:
- The lectures are on Wednesdays (KE E-101) and Fridays (KE A-101) (from 10.15 to 12.00) only in-person in (see the full schedule here)
- The lecturers are Vinay Setty and Petra Galuscakova.
- Labs:
- The labs are in-person on Wednesdays (12.15 - 14.00) in KE D-302.
- Gabriel Iturra-Bocaz is the teaching assistant for the course.
- Make an appointment with the teaching assistant for the help with lab.
- 3 ungraded (pass/fail) mandatory assignments for qualification to final exam.
- Each assignment must be submitted to GitHub Classroom platform and achieve at least 80% score in the tests and evaluations to be approved.
- Once 80% score is reached the student must get the assignment approved by one of the TAs or lecturers. This approval process is intentionally manual to ensure that you understand assignment submission you made and be prepared to answer any questions about it.
- Approval can be done after the deadline also but better to get it done sooner.
- Everyone has 5 slip days which you are free to spend as you wish (on any of the three assignments).
- Project:
- 40% of the final grade is assigned to the group project (working code + written report + presentation).
- 60% of the final grade is based on a written exam on Inspera with no Internet connection.
(Lab submission deadlines are marked with bold.)
| Week | Date | Topic | Lecturer | Resources | Comments |
|---|---|---|---|---|---|
| 2 | 07.01.2026 | Introduction (What is generative AI) | VS | Generative Deep Learning Ch. 1 | |
| – Course structure | slides | ||||
| – Assignments / project | |||||
| – Probability and Statistics basics | slides | ||||
| – Generative modeling | slides | ||||
| 07.01.2026 | Lab | GIB | |||
| – Setup Python / VS Code | |||||
| 09.01.2026 | Deep learning recap | PG | Generative Deep Learning Ch. 2 | VS away | |
| – Deep neural networks | slides notebook | ||||
| – CNN | slides notebook | ||||
| 3 | 14.01.2026 | Variational Autoencoders | VS | Generative Deep Learning Ch. 3 | |
| – Autoencoders | slides | ||||
| – VAE | |||||
| 14.01.2026 | Lab | GIB | |||
| 16.01.2026 | VAE continued | VS | Generative Deep Learning Ch. 3 | ||
| 4 | 21.01.2026 | GAN 1 | VS | Generative Deep Learning Ch. 4 | |
| Why study GANs? and comparision to VAE | VS | slides | |||
| How to train GANs? | VS | ||||
| GAN failures | VS | ||||
| GAN vairations (WGAN and CGAN) | VS | ||||
| 21.01.2026 | Lab | GIB | |||
| 23.01.2026 | GAN 2 | VS | Generative Deep Learning Ch. 10 | ||
| Advanced GANs | VS | slides | |||
| ProGAN | VS | ||||
| StyleGAN | VS | ||||
| 5 | 28.01.2026 | Language Models – part 1 | VS | Generative Deep Learning Ch. 5 | |
| 28.01.2026 | RNNs | VS | slides | ||
| 28.01.2026 | Assignment 1 deadline | ||||
| 29.01.2026 | Language Models – part 2 | VS | slides | ||
| 6 | 04.02.2026 | No lecture | Department workshop | ||
| 04.02.2026 | Lab | GIB | |||
| 06.02.2026 | LLM foundations – part 1 | VS | Generative Deep Learning Ch. 9 and RLHF book | ||
| Transformers (pre-training and RLHF) | VS | slides | |||
| 7 | 11.02.2026 | LLM foundations – part 2 | VS | slides | |
| 11.02.2026 | Lab | GIB | |||
| 13.02.2026 | LLM RLHF continued | VS | Exercises | ||
| 8 | 18.02.2026 | LLM Prompting Techniques | VS | slides Hands-On Large Language Models book | |
| 18.02.2026 | Assignment 2 deadline | GIB | |||
| 20.02.2026 | LLM fine-tuning | VS | slides | ||
| 9 | 25.02.2026 | Prompting in Ollama + LoRA fine-tuning with unsloth framework. (assignment 3) | GIB | ||
| 25.02.2026 | Lab | GIB | |||
| 27.02.2026 | Multilingual Language Models | PG | slides | ||
| 10 | 04.03.2026 | Mulitmodal Models - part 1 | PG | slides | |
| 04.03.2026 | Lab | GIB | |||
| 06.03.2026 | Mulitmodal Models - part 2 | PG | slides | ||
| 11 | 11.03.2026 | Music | PG | slides | |
| 11.03.2026 | Lab | GIB | |||
| 13.03.2026 | Knowledge and RAG | PG | slides | ||
| 12 | 18.03.2026 | Agentic generative AI | PG | slides | |
| 18.03.2026 | Assignment 3 deadline | GIB | |||
| 20.03.2026 | World models | PG | slides | ||
| 13 | 25.03.2026 | Evaluation of Generative Systems | PG | slides | |
| 25.03.2026 | Lab | GIB | |||
| 27.03.2026 | Ethics and responsible AI, fairness and bias, alignment | PG | slides | ||
| 14 | 01.04.2026 | No lecture easter week | |||
| 01.04.2026 | No lecture easter week | ||||
| 03.04.2026 | No lecture easter week | GIB | |||
| 15 | 08.04.2026 | No lecture | |||
| 08.04.2026 | Lab | GIB | |||
| 10.04.2026 | Invited talk | Jan Hajič jr. | |||
| 16 | 15.04.2026 | Invited talk ( Generative AI for scientific discovery. ) | Venktesh V | slides | |
| 16 | 15.04.2026 | Recorded talk | Understanding the EU AI Act | Tuesday 14 April 2026 10:15-11:15, at Universitetsbiblioteket | |
| 15.04.2026 | Lab | GIB | |||
| 17.04.2026 | Invited talk | Shadi Saleh | |||
| 17 | 22.04.2026 | Project presentation + Q&A | |||
| 22.04.2026 | Project presentation + Q&A | ||||
| 24.04.2026 | Project presentation + Q&A | ||||
| 18 | 01.05.2026 | Project + report due |