Supervised Learning

Supervised learning is a machine learning paradigm where an algorithm is trained on a labeled dataset, meaning it is provided with input-output pairs. The algorithm learns to map the input data to the corresponding output labels, allowing it to make predictions or classifications on new, unseen data. The goal of supervised learning is to generalize from the training data and make accurate predictions on new, previously unseen examples.

Topics

• LDA: Linear Discriminant Analysis
• QDA: Quadratic Discriminant Analysis
• Logistic Regression: classification algorithm
• Perceptron: the simplest form of an artificial neuron
• SVM (Support Vector Machine): classification algorithm
• DNN for classification
• Decision trees
• Convolutional Neural Networks
• Recurrent Neural Network
• Expectation-Maximization Algorithm
• K-Nearest Neighbors