This repository contains the results of the AI Winter School, which was held from January 4th to January 22nd, 2023. The school had eight participants who worked on various projects related to artificial intelligence. Below is a summary of their projects, including links to their presentations and colab notebooks.
The Beginner group consists of students who are new to the field of data science. During their studies, they were tasked with applying their knowledge to four interesting data sets. They analyzed the datasets and performed all the processes of predicting through machine learning models, and wrote a Colab, an interactive python tool for sharing this process and results. The following are the results of their presentations and the information about the Colab.
| Name | Dataset name | Presentation | Colab |
|---|---|---|---|
| Minjae Chung | Pima Indians Diabetes Dataset | Link | Colab link |
| Dongin Moon | Boston house price dataset | Link | Colab link |
| Yujin Kim | Wine quality dataset | Link | Colab link |
| Jiwon Im | Wheat seeds dataset | Link | Colab link |
The Advanced group consists of students who have prior experience with data science. They were asked to prepare and present a practical session for the Beginner group during the AI Winter School. During the self-study period, they explored and presented topics of their own interest.
| Practical session | Self study | |
|---|---|---|
| Gang Hyun Kim | Artificial neural network with numpy | Reinforcement learning |
| Jong Bum Won | Convolutional neural network | Self & unsupervised learning |
| Jin Sung Oh | Machine learning | Reinforcement learning |
| Yejin Lee | SHarpley Additive exPlanations (SHAP) | Diffusion network |
The AI Winter School was a great success, and all participants gained valuable experience working on real-world AI projects. We hope that the work presented here can be useful for future researchers and developers.
We would like to express our gratitude to the Center for Biosystems and Biotech Data Science for providing the funding for this program. Additionally, we would like to extend our appreciation to the Student Intensive Research Training Program (IRTP) in the Academic Affairs team.
Most of the images used in this introduction were created through DALL·E 2.