Personal Repository for Yonsei Expanded Statistics Club (ESC) July. 2022 - Dec.2023 activities.
Advisor: Prof. Jaewoo Park
Participate in 2 Main Session and corresponding Group Studies.
Study Bayesian Statistics and Advanced Bayesian Statistics. Beginning with fundamental concepts like likelihood and priors, and progressing to more complex techniques such as Markov Chain Monte Carlo (MCMC) and Generalized Linear Models (GLM).
Textbooks
- Peter D. Hoff (2009). First Course in Bayesian Statistical Methods. Springer.
- John K. Kruschke (2014). Doing Bayesian Data Analysis. Academic Press.
- Andrew Gelman (2004). Bayesian Data Analysis. Chapman & Hall/CRC.
Study Convex Optimization and its application for machine learning. Covered fundamental concepts of convexity, alongside practical techniques like unconstrained minimization and subgradient methods.
Textbooks
- Stephen Boyd, Lieven Vandenberghe (2004). Convex Optimization
- Suvrit Sra et al (2011). Optimization for Machine Learning
Participate in 1 domestic competition and 2 club competitions.
- Theme: This project focused on analyzing patterns in loan application app usage to predict whether users would apply for loans, employing advanced data analytics techniques to interpret user interactions within the app.
- Dataset: App usability data
- Team: TACO
- Organized by: Ministry of Science and ICT, National IT Industry Promotion Agency
- Reference: BigContest Website
- Theme: The aim of this project was to develop a predictive model for drug consumption from a Bayesian perspective, using demographic and personality indicators to estimate the likelihood of drug addiction either as a regression problem by scoring the degree of addiction, or as a classification problem by categorizing into seven levels or dichotomizing the data into non-users and users.
- Dataset: Drug Consumption, UCI, 2016
- Team: Team 4
- Organized by: Yonsei ESC
- Theme: This project was geared towards optimizing energy consumption in the manufacturing industry by designing models that integrate data on workforce numbers, labor costs, production volumes, and energy costs to derive the most efficient manufacturing plans.
- Dataset: Resource Optimization AI Dataset, KAIST, 2021
- Team: Team 5
- Organized by: Yonsei ESC
Leadership in Group Study for Beginner Programmers. Providing guidance on basic concepts of data structures, assisting in the presentation of team members' topic, and facilitating discussions among them.
| Week | Summary |
|---|---|
| 1st | Topic 1: Big-O Notation |
| Topic 2: Array, List, Stack, Queue | |
| Topic 3: Tree | |
| 2nd | Topic 1: Tree |
| Topic 2: Graph | |
| 3rd | Topic 3: Sorting |
Leadership in Group Study for Students Entering Artificial Intelligence. Guiding each member in developing presentations on their topics of interest. Facilitating discussions, engaging the team in insightful debate, and addressing their questions to deepen understanding and enhance collaborative learning.
Participate in the linear algebra study.
For more information about ESC, please check:
For detailed information about my main sessions, you can visit ESC official repositories: