# [Bandit] 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.

# [Bandit] 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.

# [Real Analysis] Ch 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.
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.
The [Statistical Learning] series of posts are my summary of The Elements of Statistical Learning (ESL) and a memo on the lecture Advanced Data Mining (Spring, 2021) by Prof. Yongdai Kim. Main goal of the lecture is to interpret classical machine learning models in terms of statistics and decision theoretic framework.