"Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive."
A subset of Artificial Intelligence that focuses on developing algorithms that can learn from data. Machine Learning can be used to automatically generate virtual environments and characters.
Basic programming skills: It's essential to have a solid foundation in programming languages like Python or R.
Linear algebra and calculus: Machine learning involves a lot of matrix algebra and optimization, so a good understanding of linear algebra and calculus is essential.
Statistics and probability: Understanding statistics and probability is critical to interpreting the results of machine learning algorithms.
Data preprocessing: Cleaning and preparing data for analysis is a crucial step in the machine learning process.
Machine learning algorithms: There are various types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning, and it's essential to understand how they work.
Deep learning: Deep learning is a type of machine learning that uses neural networks, and it's widely used in many applications like computer vision and natural language processing.
Optimization techniques: Gradient descent and stochastic gradient descent are powerful optimization methods used in many machine learning algorithms.
Model evaluation: Evaluating the performance of machine learning models is critical to selecting the best model for a specific problem.
Feature selection: The process of selecting the most important features from the dataset is vital to improve the model's accuracy.
Overfitting and underfitting: Underfitting occurs when the model is too simple, and overfitting occurs when it's too complex, and it's essential to balance these factors.
Ensemble methods: Ensemble methods combine multiple models to improve accuracy and generalization performance.
Natural language processing: Natural language processing (NLP) is a subfield of machine learning that focuses on the processing and understanding of human language.
Computer vision: Computer vision is another subfield of machine learning that focuses on processing and understanding visual information.
Deep reinforcement learning: Deep reinforcement learning combines deep learning and reinforcement learning, and it's widely used in game AI and robotics.
Transfer learning: Transfer learning involves reusing knowledge from pre-trained models to improve the accuracy and efficiency of new models.
Supervised Learning: This type of machine learning involves using labeled data to train algorithms to make predictions or classifications. In this technique, the algorithm learns to map inputs to outputs by minimizing the difference between the predicted and actual output.
Unsupervised Learning: This type of machine learning involves using unlabeled data to learn patterns by grouping and clustering similar data points. It involves finding hidden structure or relationships in the data without any explicit output.
Semi-Supervised Learning: This technique is a combination of supervised and unsupervised learning. It uses labeled and unlabeled data to train algorithms by making predictions and clustering data points. It is useful when obtaining large amounts of labeled data is costly or time-consuming.
Reinforcement Learning: This type of machine learning involves learning through trial and error by rewarding or punishing an algorithm for its actions. It is commonly used in robotics and games, and the algorithm learns to make the best possible decision by maximizing a reward function.
Deep Learning: This technique is a subset of neural networks that involves multiple layers of interconnected nodes, allowing for complex and abstract representations of the input data. It is capable of solving complex problems such as object detection and recognition, natural language processing, and speech and image recognition.
Transfer Learning: This technique involves using an existing trained model as a starting point for a new task. It saves time and computational resources by reusing already learned features from existing models to perform new tasks.
Active Learning: This technique involves an iterative process where a machine learning model selects the most informative data points to label and then learns from the feedback to improve its accuracy. It is useful when labeled data is scarce, and collecting more data is expensive or impossible.
Online Learning: This technique involves incremental learning, where the model updates its parameters in real-time based on new data. It is commonly used in streaming data scenarios, such as online advertising or sensor networks.
Bayesian Learning: This technique involves using Bayesian probability theory to model uncertainty in the data and adjust predictions based on new evidence. It is useful when data has missing or incomplete values.
Ensemble Learning: This technique involves combining multiple models to produce a better overall result. It can be used to increase the accuracy, robustness, or speed of the model.
Gaussian processes: This technique involves constructing a probabalistic model to discover underlying functions in the input data. It can be used to make predictions or discover patterns in data which does not have known supervision.
Clustering: This type of machine learning involves grouping similar data points based on similarity or distance. It is useful in identifying different groups of behavior or categorizing datasets based on similar patterns.
"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."