Deterministic to Random: The Role of Probability in AI and Data Science

Introduction

An experiment refers to an operation or activity that can produce some well-defined outcome(s).

Types of experiments:

• Deterministic Experiments
• Random (or Probabilistic) Experiments

Deterministic Experiments

These experiments have a fixed outcome or result, no matter how many times they are repeated under the same conditions.

Random Experiments

These experiments do not produce the same outcome every time, even when repeated under identical conditions. Examples include:

• Tossing a Coin: The result can be either heads or tails, with a probability of 50% each.
• Rolling a Die: Produces a number from 1 to 6, each with a probability of \(\frac{1}{6}\).
• Drawing a Card: The probability of drawing a specific card (e.g., Ace of Spades) is \(\frac{1}{52}\).

Applications of Probability in AI and Data Science

• Predictive Modeling: Estimating future events based on historical data (e.g., fraud detection).
• Machine Learning Algorithms: Many rely on probability, such as Naive Bayes and Hidden Markov Models.
• Natural Language Processing: Probability models predict the likelihood of word sequences (e.g., predictive text).
• Data Sampling and Hypothesis Testing: Probability helps validate models and determine confidence intervals.

Resources

PDF Presentation

randomexperiments

Video