Introduction to Computers: Lecture 6

Flashcards covering key terms and concepts related to artificial intelligence and machine learning as discussed in Lecture 6.

Artificial Intelligence (AI)

A field of computer science that aims to create systems capable of performing tasks that normally require human intelligence.

Machine Learning (ML)

A subfield of AI focused on the development of algorithms that allow computers to learn from and make predictions based on data.

Alan Turing

A mathematician and logician who is considered the father of computer science and artificial intelligence.

Neural Network

A computational model inspired by the way biological neural networks in the human brain process information.

Supervised Learning

A type of machine learning where the model learns from labeled training data to make predictions for new data.

Unsupervised Learning

A type of machine learning where the model learns patterns from unlabeled data without predefined outputs.

Semi-supervised Learning

A machine learning approach that combines a small amount of labeled data with a large amount of unlabeled data for training.

Reinforcement Learning (RL)

A type of machine learning where an agent learns to make decisions based on feedback from interacting with an environment.

Backpropagation Algorithm

A method used in neural networks to calculate gradients and update weights based on errors.

Data Preprocessing

The stage of machine learning focusing on collecting, cleaning, and preparing data for training models.

Model Testing

The process of evaluating a trained machine learning model's performance on new, unseen data.

Deep Learning

A subset of machine learning that uses neural networks with many layers to analyze various factors of data.