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Yonsei_ESC

Personal Repository for Yonsei Expanded Statistics Club (ESC) July. 2022 - Dec.2023 activities.
Advisor: Prof. Jaewoo Park


Contents


👩‍🏫 Club Sessions

Participate in 2 Main Session and corresponding Group Studies.

1. Bayesian Statistics (July. 2022 - Dec. 2022)

2022Summer 2022Fall

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.

2. Convex Optimization (Jan. 2023 - June. 2023)

2023Spring 2023Winter

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


🖥️ Team Projects

Participate in 1 domestic competition and 2 club competitions.

1. 2022 BigContest (Aug. 2022 - Oct. 2022)

Loan Application Prediction Analysis using App Usability Data

  • 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

2. 2022 FinalContest (Oct. 2022 - Nov. 2022)

Regression or Classification Problem (Logistic Regression / GLM)

  • 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

3. 2023 FinalContest (May. 2023 - June. 2023)

Optimize Energy Consumption in Industry

  • 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

🏃‍♀️ Teaching Assistants

1. Data Structure & Algorithm (Jan. 2023 - Feb. 2023)

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

2. Deep Learning & Machine Learning Study (July.2023 - Nov.2023)

Notion

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.


🤓 Group Study

Linear Algebra (Jan. 2023 - Feb. 2023)

Participate in the linear algebra study.


🗂️ Official Repository

For more information about ESC, please check:
ESCGithub ESCYoutube ESCInstagram

For detailed information about my main sessions, you can visit ESC official repositories:

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