Machine Learning

Introduction

A learning model is a parameterized function whose parameters are learned from data. When data is processed through a learning model it's outputs are often approximate relationships or make predictions.

Problem Classes

There are numerous algorithms for predicting continuous variables or categorical variables from a set of continuous predictors and/or categorical factor effects.

Regression-type problems.

Regression-type problems are generally those where we attempt to predict the values of a continuous variable from one or more continuous and/or categorical predictor variables.

Classification-type problems.

Data_classification

Classification-type problems are generally those where we attempt to predict values of a categorical dependent variable (class, group membership, etc.) from one or more continuous and/or categorical predictor variables. There are a number of methods for analyzing classification-type problems and to compute predicted classifications, either from simple continuous predictors (e.g., binomial or multinomial logit regression in GLZ), from categorical predictors (e.g., Log-Linear analysis of multi-way frequency tables), or both (e.g., via ANCOVA-like designs in GLZ or GDA).

Tree-Based Solutions

Tree methods are nonparametric and nonlinear + Simplicity of results. -Specify Criteria for Predictive Accuracy, Selecting Splits, When to Stop Splitting.

Model Types By Statistical Role

Generative

Gaussian Mixture Models
Hidden Markov Models
Naive Bayes
GANS (when parameterized accordingly)

Discriminative

NN
SVM
Decision Trees
Logistic Regression

Descriptive

Clustering (k-means, DBSCAN)
Association rule mining
PCA or factor analysis

Learning Model Paradigms

Supervised:

labeled inputs; regression and classification.

Unsupervised

latent structure; clustering, density estimation, manifold learning.

Semi-supervised

joint usage of labeled + unlabeled samples.

Self-supervised

surrogate labels from transformations; pretraining regimes.

Reinforcement learning

sequential decision processes maximizing cumulative reward.

Charles Karpati | Machine Learning