Supervised, Unsupervised, and Reinforcement ML

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Applications

There are many applications for machine learning, including:

  • Agriculture
  • Anatomy
  • Adaptive website
  • Affective computing
  • Astronomy
  • Automated decision-making
  • Banking
  • Behaviorism
  • Bioinformatics
  • Brain–machine interfaces
  • Cheminformatics
  • Citizen Science
  • Climate Science
  • Computer networks
  • Computer vision
  • Credit-card fraud detection
  • Data quality
  • DNA sequence classification
  • Economics
  • Financial market analysis
  • General game playing
  • Handwriting recognition
  • Healthcare
  • Information retrieval
  • Insurance
  • Internet fraud detection
  • Knowledge graph embedding
  • Linguistics
  • Machine learning control
  • Machine perception
  • Machine translation
  • Marketing
  • Medical diagnosis
  • Natural language processing
  • Natural language understanding
  • Online advertising
  • Optimization
  • Recommender systems
  • Robot locomotion
  • Search engines
  • Sentiment analysis
  • Sequence mining
  • Software engineering
  • Speech recognition
  • Structural health monitoring
  • Syntactic pattern recognition
  • Telecommunication
  • Theorem proving
  • Time-series forecasting
  • Tomographic reconstruction
  • User behavior analytics

In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning. In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. Machine learning was recently applied to predict the pro-environmental behavior of travelers. Recently, machine learning technology was also applied to optimize smartphone's performance and thermal behavior based on the user's interaction with the phone. When applied correctly, machine learning algorithms (MLAs) can utilize a wide range of company characteristics to predict stock returns without overfitting. By employing effective feature engineering and combining forecasts, MLAs can generate results that far surpass those obtained from basic linear techniques like OLS