Artificial Intelligence Basics

Introduction to AI

• Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence.
• Two primary approaches to AI programming: rule-based programming and data-driven programming.

Types of AI

• Machine Learning (ML): a subset of AI that enables machines to learn from data without being explicitly programmed.
• Narrow or Weak AI: designed to perform a specific task, such as facial recognition or language translation.
• General or Strong AI: hypothetical AI that possesses human-like intelligence and can perform any intellectual task.

Machine Learning Types

• Supervised Learning: trained on labeled data to learn patterns and make predictions.
• Unsupervised Learning: trained on unlabeled data to discover patterns and relationships.
• Reinforcement Learning: learns through trial and error by receiving rewards or penalties.
• Semi-Supervised Learning: combination of supervised and unsupervised learning.

AI Lifecycle

• Defining the problem: identifying a problem or opportunity that AI can solve.
• Preparing Data: collecting, cleaning, and preprocessing data for model training.
• Training: using data to train and develop an AI model.
• Testing: evaluating the performance of the trained model.
• Evaluating the Model: assessing the model's accuracy, confidence, and bias.

Machine Learning: Data Preparation

• Data Cleaning: handling duplicates, missing data, and invalid data to ensure data quality.

Machine Learning: Testing

• Testing for Bias: identifying and mitigating biases in the model to ensure fairness.
• Measuring Accuracy and Confidence: evaluating the model's performance and predicting uncertainty.

Decision Trees

• A decision tree is a graphical representation of decisions and their consequences, including chance node events and utility node payoffs.
• Decision trees can be used to solve problems with ML models by identifying the most important features and relationships in the data.

Bias in Machine Learning

• Bias in, Bias out: the concept that biased data leads to biased models and predictions.
• Importance of addressing bias in ML models to ensure fairness and accuracy.

Test your understanding of AI fundamentals, including types of AI, machine learning, and the AI lifecycle. Covers data preparation, testing, and evaluating models.