Organized by Ho-min Park (Ph.D. Student, Ghent University)
We are thrilled to announce the GUGC AI Winter School 2024, following the resounding success of our AI Summer School 2023. This Winter School is designed as an intensive 6-week program from January 5th to February 18th, aimed at advancing the knowledge and application of artificial intelligence (AI) among participants. Our goal is to provide an enriching environment for both theoretical and practical learning, enabling participants to develop and refine their AI projects.
For those interested in the previous editions and their resources, information can be found below:
Join us as we delve deeper into the world of AI, building on the foundations laid in previous sessions and exploring the cutting-edge developments in this dynamic field.
The Winter School kicked off with two weeks of lectures and workshops, covering a wide range of topics from AI fundamentals to advanced subjects. These sessions, detailed below, were crafted to provide participants with a comprehensive understanding of AI technologies and their real-world applications.
During the first two weeks, the following schedule was observed, featuring a mix of theoretical and hands-on sessions:
| Day | Date | Morning (10:00 - 12:00) | Afternoon (14:00 - 16:00) | Presenter |
|---|---|---|---|---|
| Mon | January 8th | Orientation & Quiz (session 1) | Environment setting and Python review | Ho-min Park |
| Tue | January 9th | Computer network and computer structure | Machine learning practical (1) | Ho-min Park |
| Wed | January 10th | Data mining & machine learning | Machine learning practical (2) | Ho-min Park |
| Thu | January 11th | Interpretability methods | ANN practical with NumPy | Dahee Kim |
| Fri | January 12th | Artificial Neural Network | ANN practical with NumPy | Taekeun Kim |
| Mon | January 15th | Convolutional Neural Network | CNN Practical | Dongin Moon |
| Tue | January 16th | Evaluation, loss, and optimizations | CNN practical + | Dongin Moon |
| Wed | January 17th | RNN and Transformer | Transformer Practical | Ho-min Park |
| Thu | January 18th | Un- and self-supervised learning | Reinforcement Learning | Ho-min Park |
| Fri | January 19th | Generative Adversarial Network | Final exam | Ho-min Park |
This schedule provided a structured and in-depth exploration of various AI topics, allowing for a progressive build-up of knowledge and skills.
Following the initial two weeks, participants engaged in a four-week self-study period, during which they worked individually or in teams on their AI projects. This phase encouraged participants to apply their acquired knowledge to practical challenges, fostering innovation and creativity.
The program culminated in a two-day seminar of presentations, where each participant or team showcased their project outcomes alongside peers from other training programs within the same research center. This collaborative seminar provided an opportunity for a rich exchange of ideas, feedback, discussion, and recognition of the hard work and achievements of all involved. Presentation Materials
The Winter School was specifically designed for undergraduate students at GUGC who were keen on deepening their understanding and skills in artificial intelligence. This program aimed to cater to students across various disciplines within GUGC, providing them with the opportunity to explore AI concepts and apply them to real-world scenarios. Whether participants were beginners in AI or had some foundational knowledge, this Winter School was tailored to enhance their learning experience and prepare them for future academic or industry challenges in the field of AI.
In a unique approach to fostering a community of continuous learning and mentorship, the GUGC AI Winter School 2024 featured a distinguished panel of instructors drawn from the previous year's Beginner group, who had now advanced in their knowledge and skills to take on the role of educators and mentors. These advanced students led lectures and practical sessions alongside seasoned academics and industry experts. This innovative structure not only enriched the learning experience with fresh perspectives and relatable insights but also empowered students by highlighting the path from learner to leader in the AI field.
Details on the names, backgrounds, and lecture topics of these student instructors, as well as the professional experts they collaborated with, were provided to give participants a comprehensive overview of the diverse expertise available to support their AI journey during the Winter School.