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ML Definitions

[Oklahoma State - Forbes]

 - Overview

Machine learning is a subset of artificial intelligence. It enables systems to learn and improve themselves through experience without programming. It uses statistics to find patterns in large amounts of data, which can be many things like numbers, images or words, or whatever you have. If it can be stored digitally, it can be fed into a machine learning algorithm. To train the system, you need a lot of data. If you think you will use 100-200 data (eg images) to train the system, it will give output but very low accuracy. You need about 10000-15000 data for training. It depends on what you train them for. 

There are four types of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In short, in supervised learning, the input data is already defined (labeled) and you know what your output should be. In unsupervised learning, the input data is undefined (unlabeled). And you are not sure what your output is.


- Machine Learning Definitions


  • [The Pennsylvania State University]: Machine learning is concerned with the scientific study, exploration, design, analysis, and applications of algorithms that learn concepts, predictive models, behaviors, action policies, etc. from observation, inference, and experimentation and the characterization of the precise conditions under which classes of concepts and behaviors are learnable. Learning algorithms can also be used to model aspects of human and animal learning. Machine learning integrates and builds on advances in algorithms and data structures, statistical inference, information theory, signal processing as well as insights drawn from neural, behavioral, and cognitive sciences.
  • [CUNY]: Machine learning is a branch of artificial intelligence, concerned with the construction and study of systems that can learn from data. Learning means to make accurate predictions or useful decisions based on past observations and experience. Machine learning has matured to be a highly successful discipline with applications in many areas such as natural language processing, speech recognition, medical image analysis, document image analysis, computer vision, or predicting properties of drugs and genes. The anthropomorphic term learning of the machine learning phrase means being able to predict some unobserved components of the data given some observed components of the data. Other terms related to machine learning are pattern recognition and big data analysis. The data used in machine learning may be numeric or symbolic and typically has the form of an N-tuple, a graph, network or relation. 
  • [CMU]: "Machine Learning is a scientific field addressing the question "How can we program systems to automatically learn and to improve with experience?" We study learning from many kinds of experience, such as learning to predict which medical patients will respond to which treatments, by analyzing experience captured in databases of online medical records. We also study mobile robots that learn how to successfully navigate based on experience they gather from sensors as they roam their environment, and computer aids for scientific discovery that combine initial scientific hypotheses with new experimental data to automatically produce refined scientific hypotheses that better fit observed data. To tackle these problems we develop algorithms that discover general conjectures and knowledge from specific data and experience, based on sound statistical and computational principles. We also develop theories of learning processes that characterize the fundamental nature of the computations and experience sufficient for successful learning in machines and in humans."
  • [University of Massachusetts, Amherst]: Machine learning is the computational study of pattern discovery and skill acquisition. This includes methods by which artificial agents can improve their behavior while interacting with their environments, for example, by learning effective behavioral strategies from experience or by improving the knowledge structures forming the basis of their decisions. Machine learning also includes data mining techniques for finding patterns in large bodies of data. Specific research topics in computer science include learning conceptual structures through developmental processes; improving control of stochastic and nonlinear dynamic systems through reinforcement feedback; learning robot control strategies; finding patterns in large bodies of data represented in graphical form, including social networks; extracting or retrieving information in natural language; classification of genetic data; and using learning methods for improving discrete optimization algorithms. 
  • [Amazon Machine Learning]: Machine learning (ML) can help you use historical data to make better business decisions. ML algorithms discover patterns in data, and construct mathematical models using these discoveries. Then you can use the models to make predictions on future data. For example, one possible application of a machine learning model would be to predict how likely a customer is to purchase a particular product based on their past behavior.
  • Literally, with machine learning, you show computer how to do certain things. For example, you want a computer to know how to cross a road. With conventional programming, you would give it a very precise set of rules, telling it how to look left and right, wait for cars, use pedestrian crossings, etc., and then let it go. With machine learning, you’d instead show it 10,000 videos of someone crossing the road safely (and 10,000 videos of someone getting hit by a car), and then let it do its thing. 




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