"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
A set of statistical and computational techniques for analyzing data and making predictions or classifications, based on experience with similar data sets.
Probability Theory: Probability theory deals with the study of random events and their probability of occurrence.
Statistics: Statistics is the application of mathematical methods for collection, analysis and interpretation of data.
Linear Algebra: Linear algebra deals with the study of linear equations and their applications in various fields.
Calculus: Calculus deals with the study of continuous change and is used extensively in machine learning.
Optimization: Optimization is the process of finding the best solutions for given problems.
Regression Analysis: Regression analysis is a statistical technique that is used for estimating the relationship between two variables.
Clustering: Clustering is a technique of grouping similar objects together.
Classification: Classification is the process of identifying which category an object belongs to.
Neural Networks: Neural networks are a type of machine learning algorithm that are based on the structure of the human brain.
Decision Trees: Decision Trees are used for making decisions based on multiple variables.
Data Mining: Data mining is the process of discovering patterns in large data sets.
Time Series Analysis: Time series analysis is a statistical technique used for analyzing time series data.
Principal Component Analysis: Principal component analysis is a technique used for reducing the dimensionality of data sets.
Text Mining: Text mining is the process of extracting relevant information from unstructured textual data.
Natural Language Processing: Natural language processing is the study of how computers can process and understand human language.
Computer Vision: Computer vision is the field of study that deals with the ability of computers to interpret visual information.
Reinforcement Learning: Reinforcement learning is a type of machine learning in which an agent learns to behave in an environment through trial and error.
Bayesian Statistics: Bayesian statistics is a theory in the field of statistics in which the probability of a hypothesis is updated as evidence is acquired.
Deep Learning: Deep learning is a type of machine learning that uses artificial neural networks with multiple layers.
Support Vector Machines: Support Vector Machines is a type of machine learning algorithm that is used for classification and regression analysis.
Supervised Learning: This type of Machine Learning involves input/output data pairs, where the objective is to train a model using labeled data to classify new data instances or predict the output of the new input instances.
Unsupervised Learning: In this type of Machine Learning, the objective is to find patterns, groupings, or structure in the data without any labels. Unlike supervised learning, the model does not have any target variable available to relate with the input data.
Semi-Supervised Learning: This type of Machine Learning involves a mix of labeled and unlabeled data. The model learns from labeled data to make predictions on unlabeled data. The objective of this type of learning is to make use of labeled data more efficiently.
Reinforcement Learning: This type of Machine Learning is used in situations where the model needs to learn from its own experiences. This is commonly used in game playing agents or robots, where the agent learns from its interaction with the environment.
Deep Learning: Deep learning involves creating and training large artificial neural networks that can automatically learn complex patterns in data. This method of Machine Learning is suitable for image or speech recognition applications.
Bayesian Learning: This type of Machine Learning is based on Bayesian probability theory. It involves building a model that estimates the probability distribution of an unknown variable given the observed data.
Online Learning: This type of Machine Learning involves continuous learning from new data that is fed into the model. For example, spam filter software that learns from the user's email behavior and classifies mails as spam.
Instance-Based Learning: This type of Machine Learning involves storing a set of examples that the model can use to make predictions on new instances. The model finds the closest instance in the dataset and uses its corresponding output value as a prediction.
Decision Tree Learning: This is a type of supervised learning algorithm used for classification problems. It involves creating a tree-like model of decisions and their possible consequences. Each node in the tree represents a decision and the branches represent the possible outcomes.
Ensemble Learning: This type of Machine Learning involves aggregating multiple models to obtain a better prediction. This can be achieved through methods such as bagging or boosting.
Transfer Learning: Transfer learning involves reusing features of a model trained in one task to aid in the training of another task. For example, a pre-trained CNN could be fine-tuned for a new image classification task.
Time-series Analysis: This type of Machine Learning involves predicting future values based on past trends in a time-series data. It is used in forecasting applications in finance, economics, and project management.
Association Rule Learning: This type of Machine Learning is used to find the relationship between variables in large datasets. It finds the rules that describe the relationships between the variables, which can be used for making predictions.
Dimensionality Reduction: This type of Machine Learning involves reducing the number of input variables to a model by finding the most important variables. Principal Component Analysis is a popular method used for this.
Clustering: Clustering is an unsupervised learning algorithm used for grouping similar data points. The objective is to group data points that have similar characteristics.
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"Recently, generative artificial neural networks have been able to surpass results of many previous approaches."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"where it is too costly to develop algorithms to perform the needed tasks."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"ML is known in its application across business problems under the name predictive analytics."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."
"the problems are solved by helping machines 'discover' their 'own' algorithms without needing to be explicitly told what to do by any human-developed algorithms."
"Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine."
"development of algorithms by human programmers would be cost-prohibitive"
"generative artificial neural networks have been able to surpass results of many previous approaches."
"Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning."
"Machine-learning approaches have been applied to...medicine."
"helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms."
"The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods."
"where it is too costly to develop algorithms to perform the needed tasks."
"Although not all machine learning is statistically based, computational statistics is an important source of the field's methods."