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ML Definitions and Concept

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- ML Definition: [The Pennsylvania State University] 

Machine learning (ML) 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. ML 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.

- ML Definition: [CUNY]

Machine learning (ML) is a branch of artificial intelligence (AI), 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. 

ML 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 ML phrase means being able to predict some unobserved components of the data given some observed components of the data. Other terms related to ML are pattern recognition and big data analysis. 

The data used in ML may be numeric or symbolic and typically has the form of an N-tuple, a graph, network or relation. 

- ML Definition: [CMU] 

ML 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.

- ML Definition: [University of Massachusetts, Amherst]

Machine learning (ML) 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. 

ML 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. 

[Häggvik, Sweden]

- When To Use Machine Learning

It’s important to remember that ML can’t solve every type of problem. In some cases, powerful solutions can be developed without using ML techniques. For example, ML is not required if you can determine a target value by using simple rules, calculations, or predetermined steps that can be programmed without any data-driven learning. 

Use machine learning when:

  • You can't write rules: Many human tasks (such as identifying whether an email is spam) cannot be adequately addressed using simple (deterministic), rules-based solutions. There are many factors that may affect the answer. It quickly becomes difficult for humans to accurately encode rules when they depend on too many factors and many of them overlap or require very fine tuning. You can use ML to effectively solve this problem.
  • You can't scale: You might be able to manually identify a few hundred emails and determine if they are spam. However, with millions of emails, this task becomes tedious. Machine learning solutions can effectively handle large-scale problems.


- Building ML Applications

Machine learning (ML) is a subset of AI that uses data and past experiences to improve accuracy and build software and applications. ML applications learn from data and are designed to provide accurate results. 

Here are some steps for building ML applications:

  • Define the problem
  • Assemble a team
  • Define the architecture
  • Pick a tech stack
  • Prepare data
  • Build, train, and validate ML models
  • Deploy the model
  • Evaluate and optimize model accuracy


Here are some other things to consider when building ML applications:

  • Feature engineering: This involves selecting, aggregating, or extracting features from raw data to map it to ML features.
  • Data collection and cleaning: Not all collected data is useful for a ML application. Irrelevant data may need to be cleaned, which can affect the accuracy of prediction.
  • Evaluation: Evaluating a model is a major part of building an effective ML model. The most common classification evaluation metric is accuracy.
  • Model serving: The architecture of the ML application should be designed, a serving strategy should be established, and a test suite should be created for the future ML model.  

- Main Elements and Concepts of ML

Machine learning (ML algorithms have three main elements:
  • Representation: How the model looks and how knowledge is represented.
  • Evaluation: How good models are differentiated and how programs are evaluated.
  • Optimization: The process for finding good models and how programs are generated.


Here are some ML concepts:

  • Logistic regression: A ML technique that is good for binary classification problems. It uses a logistic function at its core.
  • Decision tree: A ML technique that uses rules and conditions to solve classification problems. It involves dividing input data into two or more homogeneous data sets based on defining attributes.
  • Support vector machine: A ML method that uses statistical theory to solve fitting accuracy and generalization problems. It is used in pattern recognition, information security, and data fitting.
  • Naive Bayes: A ML algorithm that is used when the output variable is discrete. It is driven by the Bayes Theorem.
  • Random forest: A ML process that consists of many decision trees. A decision tree is a tree-like structure where each internal node represents a test on the input attribute.
  • Clustering: A ML technique that involves grouping data points. It is an unsupervised learning method and a famous technique for statistical data analysis.
  • Hyperparameters: An integral part of machine learning code that lets you control the code without directly modifying it.
  • Gradient descent: A famous optimization technique that is used in machine learning and in deep learning. Its main purpose is to minimize the cost function.

[More to come ...]

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