3. FC | Machine Learning Foundations

Flashcards covering key concepts and vocabulary from the Machine Learning Foundations lecture.

Machine Learning

A subset of artificial intelligence that uses algorithms to learn from data and make predictions.

Supervised Learning

A machine learning approach that uses labeled data to train models; includes regression and classification.

Unsupervised Learning

A machine learning approach that discovers patterns in unlabeled data through clustering.

Reinforcement Learning

A machine learning approach where agents learn through interaction with an environment using rewards and penalties.

Training and Inference

The process of teaching a model with data and then using it to make predictions.

Loss Function

Measures the difference between predicted and actual values to optimize model performance.

Linear Regression

A supervised learning method used for predicting continuous values, fitting a straight line through data points.

Logistic Regression

A supervised learning technique used for binary outcomes predicting class labels using a sigmoid function.

Clustering

A method in unsupervised learning that groups similar items based on their characteristics.

Standardization

The process of transforming data to have a mean of 0 and a standard deviation of 1 to ensure features are on the same scale.

Train-Test Split

A method for dividing a dataset into a training set and a testing set to validate model performance.

Accuracy Score

A metric used to assess the performance of a machine learning model by measuring the ratio of correctly predicted instances.

Euclidean Distance

A common metric used in clustering to measure the similarity between data points based on their features.

Policy

In reinforcement learning, the strategy that maps states to actions in order to maximize cumulative rewards.

Q-learning

A reinforcement learning algorithm that enables an agent to learn optimal actions through trial and error.