Reference the problem Suppose you are at a casino and have a choice between N slot machines. Each of the N slot machines (bandits) has an unknown probability of letting you win. i.e. Bandit 1 may have P(win) = 0.9. Bandit 2 may have P(win) = 0.3. We wish to maximize our winnings by playing […]
ML: Bayesian Bandit
Caveman Learning Augmented Analytics
John is a data scientist that working on company X. Every morning, John would bought coffee, came to the office, sit, start looking and raw data collected by the system, discuss with his colleagues Sarah, cleaning up data, created the ETL process to automate data cleanups, try out models, then at the end of working […]