What types of Machine Learning, if any, best describe the following scenario: A computer develops a strategy for playing Tic-Tac-Toe repeatedly. The computer learns through trial-and-error search where it adjusts its strategy based on reward received for each observerd state and performed action. Supervised Learning Reinforcement Learning Not a machine learning scenario Unsupervised Learning
What types of Machine Learning, if any, best describe the following scenario: A computer develops a strategy for playing Tic-Tac-Toe repeatedly. The computer learns through trial-and-error search where it adjusts its strategy based on reward received for each observerd state and performed action. Supervised Learning Reinforcement Learning Not a machine learning scenario Unsupervised Learning
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Transcribed Image Text:**Scenario: Identifying the Type of Machine Learning**
**Description:**
A computer develops a strategy for playing Tic-Tac-Toe repeatedly. The computer learns through a trial-and-error search where it adjusts its strategy based on the reward received for each observed state and performed action.
**Question:**
What types of Machine Learning, if any, best describe the following scenario?
**Options:**
- ○ Supervised Learning
- ○ Reinforcement Learning
- ○ Not a machine learning scenario
- ○ Unsupervised Learning
**Explanation:**
In this scenario, the computer employs trial-and-error methods and receives rewards based on its actions, which aligns with principles of Reinforcement Learning.

Transcribed Image Text:**Which of the following are challenges machine learning algorithms can face?**
- Models can overgeneralize to the data seen during training.
- Algorithms often act as black boxes where it is unclear what "rules" the algorithm has learned.
- Algorithms constantly need to be updated as time goes on with new rules written by humans.
- Lots of "good" quality data tends to be needed.
- When making no assumptions about a particular problem, there is no "best" algorithm to choose.
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