Artificial Intelligence Basics

Study Notes

AI Fundamentals

AI can be categorized into two programming approaches: rule-based programming, which uses pre-defined rules to make decisions, and data-driven programming, which relies on data to make predictions or classifications.

Types of AI

Machine Learning: a type of AI that enables machines to learn from data without being explicitly programmed.
Narrow AI (Weak AI): designed to perform a specific task, such as facial recognition or language translation.
General AI (Strong AI): aims to mimic human intelligence, with the ability to reason, learn, and apply knowledge across a wide range of tasks.

Types of Machine Learning

Supervised Learning: the machine is trained on labeled data to learn the relationship between input and output.
Unsupervised Learning: the machine is trained on unlabeled data to discover patterns or structure.
Reinforcement Learning: the machine learns through trial and error, receiving rewards or penalties for its actions.
Semi-Supervised Learning: a combination of supervised and unsupervised learning, where the machine is trained on both labeled and unlabeled data.

AI Lifecycle

Defining the problem: identifying the problem or opportunity that the AI model will address.
Preparing Data: collecting, cleaning, and preprocessing the data for training.
Training: using the prepared data to train the AI model.
Testing: evaluating the performance of the trained model on a separate dataset.
Evaluating the Model: assessing the model's performance and making adjustments as needed.

Machine Learning: Data Preparation

Cleaning: removing duplicates, handling missing data, and correcting invalid data to ensure the data is reliable and consistent.

Machine Learning: Testing

Testing for Bias: ensuring the model is fair and unbiased in its predictions or classifications.
Measuring Accuracy and Confidence: evaluating the model's performance using metrics such as accuracy, precision, and recall.

Machine Learning: Confidence and Accuracy

Bias in, Bias out: the model's performance is only as good as the data it's trained on, so biased data can lead to biased models.
Decision Trees: a type of machine learning model that uses a tree-like structure to classify data and make predictions.

Decision Trees

How decision trees are made: by recursively partitioning the data into smaller subsets based on the values of the input features.

Solving Problems with ML Models

ML models can be used to solve a wide range of problems, from image and speech recognition to natural language processing and recommender systems.

AI Fundamentals

AI can be categorized into two programming approaches: rule-based programming, which uses pre-defined rules to make decisions, and data-driven programming, which relies on data to make predictions or classifications.

Types of AI

Machine Learning: a type of AI that enables machines to learn from data without being explicitly programmed.
Narrow AI (Weak AI): designed to perform a specific task, such as facial recognition or language translation.
General AI (Strong AI): aims to mimic human intelligence, with the ability to reason, learn, and apply knowledge across a wide range of tasks.

Types of Machine Learning

Supervised Learning: the machine is trained on labeled data to learn the relationship between input and output.
Unsupervised Learning: the machine is trained on unlabeled data to discover patterns or structure.
Reinforcement Learning: the machine learns through trial and error, receiving rewards or penalties for its actions.
Semi-Supervised Learning: a combination of supervised and unsupervised learning, where the machine is trained on both labeled and unlabeled data.

AI Lifecycle

Defining the problem: identifying the problem or opportunity that the AI model will address.
Preparing Data: collecting, cleaning, and preprocessing the data for training.
Training: using the prepared data to train the AI model.
Testing: evaluating the performance of the trained model on a separate dataset.
Evaluating the Model: assessing the model's performance and making adjustments as needed.

Machine Learning: Data Preparation

Cleaning: removing duplicates, handling missing data, and correcting invalid data to ensure the data is reliable and consistent.

Machine Learning: Testing

Testing for Bias: ensuring the model is fair and unbiased in its predictions or classifications.
Measuring Accuracy and Confidence: evaluating the model's performance using metrics such as accuracy, precision, and recall.

Machine Learning: Confidence and Accuracy

Bias in, Bias out: the model's performance is only as good as the data it's trained on, so biased data can lead to biased models.
Decision Trees: a type of machine learning model that uses a tree-like structure to classify data and make predictions.

Decision Trees

How decision trees are made: by recursively partitioning the data into smaller subsets based on the values of the input features.

Solving Problems with ML Models

ML models can be used to solve a wide range of problems, from image and speech recognition to natural language processing and recommender systems.

Description

Test your understanding of Artificial Intelligence concepts, including types of AI, machine learning, and the AI lifecycle. Topics covered include rules-based programming, data-driven programming, and machine learning preparation and testing.