Resources
This space serves as a repository for reference materials, study notes, cheat sheets, and technical guides compiled throughout my studies and research.
Posts below are arranged chronologically—spanning probability theory, machine learning, software tools, and computational notes. You can also use the Archive to browse all articles organized by tags.
Selected notes have also been compiled into standalone PDF documents:
2. Univariate Kernel Density Estimation
From histogram, we will start discussing nonparametric kernel methods.
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2. Explore-Then-Commit algorithm
Here, we continue to describe the multi-armed bandit problem in detail. The notion of regret will be introduced. Then our first bandit algorithm, explore-then-commit (ETC) will be described.
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1. Introduction to Multi-armed Bandit
The [Bandit] series of posts is my memo on the lecture Seminar in Recent Development of Applied Statistics (Spring, 2021) by Prof. Myunghee Cho Paik. This lecture focuses on adaptive sequential decision making. To be more specific, it covers wide variants of bandit problems.
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1. Measurable Space and Integration
The [Real Analysis] series of posts is my memo on the lecture Real Analysis (Spring, 2021) by Prof. Insuk Seo. The lecture follows the table of contents of Real and Complex Analysis (3rd ed.) by Rudin, with minor changes in order.
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1. Brief Introduction to Nonparametric function estimation
The [Nonparametric] series of posts is my memo on the lecture Nonparametric Function Estimation (Spring, 2021) by Prof. Byeong U. Park. The lecture is mainly focused on kernel smoothing, while also briefly covers other nonparametric methods such as MARS.
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