Machine Learning Lecture Notes

These flashcards cover key terms and concepts from machine learning as discussed in the lecture notes.

Machine Learning (ML)

A branch of artificial intelligence that enables computers to learn from data without being explicitly programmed.

Supervised Learning

A type of machine learning where the model is trained using labeled input and output pairs.

Unsupervised Learning

A type of machine learning where the model is trained using input data without labeled responses.

Regression

A supervised learning algorithm used to predict continuous outcome variables.

Classification

A supervised learning algorithm for predicting discrete outcome variables.

Clustering

An unsupervised learning technique used to group similar data points together.

Numerical Data

Data that can be quantified and used for mathematical calculations, including interval and ratio data types.

Categorical Data

Data that can be divided into categories but not measured, including nominal and ordinal data types.

Feature Engineering

The process of using domain knowledge to select and transform variables into features that help in predicting the target variable.

K-Nearest Neighbors (KNN)

A classification algorithm that predicts the class of an instance based on the classes of its nearest neighbors.

Overfitting

A modeling error that occurs when a machine learning model captures noise in the training data rather than the intended outputs.

Underfitting

A modeling error that occurs when a machine learning model is too simple to capture the underlying trend of the data.

Gradient Descent

An optimization algorithm used to minimize the cost function by iteratively moving towards the steepest descent.

Logistic Regression

A statistical method for predicting binary classes; it uses a logistic function to model a binary dependent variable.

Cost Function

A function that measures the performance of a machine learning model; the goal is to minimize this function.

Data Cleaning

The process of correcting or removing inaccurate, incomplete, or irrelevant data.

Normalization

Scaling data into a smaller range, often used in preprocessing to improve the performance of a machine learning model.

Training Set

The portion of the dataset used to train a machine learning model.

Testing Set

The portion of the dataset used to evaluate the performance of a machine learning model.

Validation Set

A separate dataset used to tune the parameters of the model and prevent overfitting.

Model Selection

The process of selecting the most appropriate model for the data by evaluating its performance.