Seminar Generative AI
Generative artificial intelligence is ubiquitous in our daily life, generating synthetic images, text, sound tracks, and videos. Example tasks include image synthesis, image styling, text to image synthesizing, voice to text translation, text translation, and question and answering.
The aim of this seminar course is to let students learn the principles and models of generative AI via paper reading, presentation, and discussion. We provide a broad overview on the design of the state-of-the-art generative models, spanning from generative adversarial networks, diffusion models and generative pre-trained transformers (GPT). We will present an array of methodologies and techniques that can efficiently and effectively train generative models against all operational conditions.
The course materials will be based on a mixture of classic and recently published papers. The first 4 lectures, the basic concept of distributed machine learning will be covered, followed by presentations from my PhD students and the students taking this course.
Details
| Code | 12574 62574 |
| Type | Seminar |
| ECTS | 5 |
| Site | Neuchâtel |
| Track(s) |
T1 – Distributed Software Systems T6 – Data Science |
| Semester | S2024 |
Teaching
| Learning Outcomes |
|
| Lecturer(s) |
Lydia Chen |
| Language | english |
| Course Page | The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_2979513.html. |
Schedules and Rooms
| Period | Weekly |
| Schedule | Monday, 14:15 - 16:00 |
| Location | UniNE, Unimail |
| Room | B217 |
Evaluation
| Evaluation type | continuous evaluation |
Additional information
| Comment | First Lecture |