"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
Machine learning is a field of study that involves developing algorithms and statistical models that enable computers to learn and improve from data without being explicitly programmed.
Statistics: Statistical methods and concepts are fundamental in machine learning as they help to analyze and interpret data.
Linear Algebra: Linear Algebra is the mathematical framework used to solve complex data-centric problems. It is used to transform raw data into a format that can be easily analyzed.
Probability: Probability theory is essential to understand the uncertainties involved in modeling biological data.
Clustering: Clustering algorithms are used to classify objects or data points into groups based on their similarities.
Classification: Classification algorithms are used to classify objects or data points into predefined categories.
Regression: Regression is a statistical method used to identify the relationship between two variables.
Neural Networks: Neural networks are a subset of machine learning methods that are used to learn by example.
Random Forests: A random forest is a model used to classify and predict data by constructing many decision trees.
Support Vector Machines: Support Vector Machines classify data by finding an optimal hyperplane that maximizes the separation between data points.
Principal Component Analysis: Principal Component Analysis (PCA) is a statistical method used to reduce the dimensionality of data by projecting it onto a lower-dimensional space.
Deep Learning: Deep learning is a subset of machine learning methods that use deep neural networks to learn from large datasets.
Artificial Intelligence: Artificial intelligence is the study of creating intelligent machines that can solve problems that are typically associated with human cognition.
Big Data: Big Data is a term used to describe large, complex datasets that cannot be processed using traditional computing methods.
Data Mining: Data mining is the process of analyzing large datasets to discover patterns and insights.
Natural Language Processing: Natural language processing is a branch of machine learning that deals with the interpretation of human language.
Time Series Analysis: Time Series Analysis is a statistical method used to analyze and interpret data over time.
Optimization: Optimization techniques are used to optimize or improve a given objective function.
Bayesian Inference: Bayesian inference is a statistical method that involves updating beliefs based on prior knowledge and evidence.
Feature Selection: Feature selection is the process of selecting the most relevant features or variables from a dataset.
Unsupervised Learning: Unsupervised learning is a type of machine learning where the model learns from unlabeled data without any target variable.
Supervised learning: It involves training a model using labeled data that has clearly defined inputs and outputs, where the input variables are mapped to an output variable. It is often used in classification and regression problems where the model learns to predict the output variable based on input variables.
Unsupervised learning: It involves training a model on a dataset without any labeled data. The model learns to identify patterns and relationships in the data without any prior knowledge of the output variables. It is often used in clustering, anomaly detection, and pattern recognition problems.
Semi-supervised learning: It involves training a model using both labeled and unlabeled data to improve the accuracy of predictions. It is often used when the amount of labeled data is limited and costly.
Reinforcement learning: It involves learning through trial and error by receiving feedback from the environment. The model tries to find the optimal policy that maximizes the reward function.
Deep learning: It involves building complex neural networks with multiple layers that can learn hierarchical representations of data. It is often used in image and speech recognition, natural language processing, and autonomous driving.
Transfer learning: It involves transferring knowledge learned from one task to another to improve the performance of the model. It is often used in domains where labeled data is scarce, such as in medical imaging.
Bayesian learning: It involves using Bayesian statistics to update the model's probability distribution as new data becomes available. It is often used in problems where prior knowledge of the probability distribution is available.
Online learning: It involves continuously updating the model as new data arrives in a streaming fashion. It is often used in real-time applications where the model needs to adapt to changing data quickly.
Ensemble learning: It involves combining multiple models to improve the accuracy of predictions. It is often used in classification and regression problems where the models have different strengths and weaknesses.
Instance-based learning: It involves learning from specific examples instead of general rules. It is often used in problems where the domain has a large number of input features, and the model needs to focus on the relevant attributes.
"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."