Code&Data Insights

[ Adversarial Search ] : a strategy used in artificial intelligence for decision-making in competitive scenarios, such as games. It involves representing the problem as a game tree, using the Minimax algorithm, evaluation functions, and techniques like (ex) chess, checkers, and in various real-world domains like business strategy, auctions, and negotiations. : Alpha-Beta Pruning to f..

: we try to identify which nodes seems more Optimistic, and explore these first - With heuristics (useful rules or empirical knowledge) or additional information is used to find the most efficient path. - A way to visit the most promising nodes first [ Heuristic ] - A technique improves the efficiency of search - Focus on nodes that seem most optimistic acc..

: all nodes are equally optimistic, so we explore them systematically => Simple search method, without heuristics or information, explores all possible paths. · Data Structure - Open List(=frontier) - Closed List(=explored set) · Generic Search Algorithm 1) Initialize OPEN with the initial node n0 and its parent 2) Initialize CLOSED to empty 3) Repeat A) If OPEN is empty, t..

: Finding a solution from initial state to a goal state [ State Space ] · Problem is represented by 1) Initial State - Starting state 2) Set of Operators - Actions for transition between states 3) Goal test function - determine if it is matched with a goal state 4) Calculate Path cost function - Assigns a cost to a path if a path is best among others · State space ..

[ R-squared  ]R-squared  - a measure of the goodness-of-fit of a regression model. - It represents the proportion of the variance in the dependent variable that is predictable from the independent variables. - the percentage of variation explained by the relationship between two variables.  => range : 0 to 1 => R² = 1 - (SSR/SST)SSR = the sum of squared residuals (the sum of the squared differen..

The Art of Statistics: Learning from Data by David Spiegelhalter   =>David Spiegelhalter is a prominent British statistician who provides numerous examples from his professional experience in the field of statistics. He endeavors to explain these concepts in an accessible way, without relying on mathematical formulas. 2학년때 probability and statistics 과목을 들었는데, 감이 안잡혀서 책을 읽었다. 다양한 예시를 줘서 전체적인 통계학의..

Covariance : ​Covariance and Correlation, both describe the relationship between two numerical variables. How to get a covariance value between X and Y? 1) Find the deviation from X to x. (Y and y) ------------> (x- x) and (y- y) 2) multiply (x- x) and (y- y) 3) Add the values, find the sum 4) divide by n-1 -> WHY did we divide by n-1, not just n? : " Degrees of Freedom" - We can't calculate the..

[ Probability Theory ] Probability theory is the study of uncertainty. It is about the concepts from probability theory for deriving machine learning algorithms and delves(=examines) into a branch of analysis known as measure theory. 1 Elements of probability - Sample Space Ω : The set of all the outcomes of a random experiment. - Sets of Events(=event space) : F: A set whose elements A ∈ F(call..