Automated Learning (ML) Task

A Automated Learning (ML) Task is a learning task that is an automated intelligence task (which improves performance through iterative experiences with data).

• AKA: Machine Learning.
• Context:
  • Input:
    • Learning Data and optionally, a Testing Record Set to validate performance.
    • Includes several rounds of Information such as Learning Records.
  • Output:
    • A Decision Act, Prediction, or Model (e.g., Predictive Model or Cluster Model).
    • Can include an Inductive Argument or a set of actionable predictions.
  • ...
  • It can be supported by Automated Learning Systems (that implement automated learning algorithms).
  • It can (often) measure prediction performance measures and cost-benefit functions.
  • ...
  • It can range from being a Automated Supervised Machine Learning Task to being an Automated Unsupervised Learning Task, depending on ...
  • It can range from being a Real-World Automated Learning Task to being an Benchmark Automated Learning Task, depending on ...
  • It can range from being a Batch ML Task to being an Online Learning Task, depending on its execution mode.
  • It can range from being a Simple Input Learning Task to being a Complex Input Learning Task, depending on its input complexity.
  • It can range from being a Manual Learning Task to being an Automated Learning Task, depending on its automation level.
  • It can range from being a Static Learning Task to being a Reinforcement Learning Task, depending on its learning approach.
  • ...
• Examples:
  • Based on Learning Supervision Type, such as:
    • Automated Supervised Machine Learning Tasks, such as:
      • Automated Learning Spam Detection Task for email filtering
      • Automated Learning Disease Diagnosis Task for medical conditions
    • Automated Unsupervised Learning Tasks, such as:
      • Automated Learning Customer Segmentation Task for pattern discovery
      • Automated Learning Anomaly Detection Task for system monitoring
  • Based on Task Application Context, such as:
    • Real-World Automated Learning Tasks, such as:
      • Automated Learning Quality Control Task for manufacturing
      • Automated Learning Drug Discovery Task for pharmaceutical research
    • Benchmark Automated Learning Tasks, such as:
      • ImageNet Classification Task for model evaluation
      • SQuAD Question Answering Task for performance assessment
  • Based on Machine Learning (ML) Execution Mode, such as:
    • Batch ML Tasks, such as:
      • Automated Learning Price Prediction Task for historical analysis
      • Automated Learning Climate Modeling Task for offline processing
    • Online Learning Tasks, such as:
      • Automated Learning Trend Analysis Task for real-time monitoring
      • Automated Learning Stream Processing Task for continuous adaptation
  • Based on Machine Learning (ML) Input Complexity, such as:
    • Simple Input Learning Tasks, such as:
      • Automated Learning Binary Classification Task for basic categorization
      • Automated Learning Simple Regression Task for single variable prediction
    • Complex Input Learning Tasks, such as:
      • Automated Learning Multi-target Regression Task for multi-variable forecasting
      • Automated Learning Multi-modal Analysis Task for integrated processing
  • Based on Machine Learning (ML) Automation Level, such as:
    • Manual Learning Tasks, such as:
      • Human-Guided Feature Selection Task for expert optimization
      • Manual Parameter Tuning Task for controlled learning
    • Automated Learning Tasks, such as:
      • AutoML Pipeline Task for autonomous optimization
      • Automated Learning Predictive Maintenance Task for self-managed monitoring
  • Based on Machine Learning (ML) Learning Approach, such as:
    • Static Learning Tasks, such as:
      • Automated Learning Document Classification Task for fixed categorization
      • Automated Learning Sentiment Analysis Task for stable patterns
    • Reinforcement Learning Tasks, such as:
      • Automated Learning Agent Behavior Task for adaptive control
      • Automated Learning Game Strategy Task for dynamic optimization
  • Based on Task Output Type, such as:
    • Automated Learning Generation Tasks, such as:
      • Automated Learning Text Generation Task for content creation
      • Automated Learning Image Generation Task for visual synthesis
    • Automated Learning Recognition Tasks, such as:
      • Automated Learning Image Recognition Task for visual analysis
      • Automated Learning Speech Recognition Task for audio processing
  • Based on Advanced Learning Capability, such as:
    • Automated Learning Meta Tasks, such as:
      • Automated Learning Few-shot Learning Task for limited data scenarios
      • Automated Learning Transfer Learning Task for domain adaptation
    • Automated Learning Interactive Tasks, such as:
      • Automated Learning Active Learning Task for selective sampling
      • Automated Learning Online Learning Task for continuous update
  • Based on Domain Application Area, such as:
    • Automated Learning Scientific Tasks, such as:
      • Automated Learning Protein Folding Task for biological structure
      • Automated Learning Material Design Task for engineering
    • Automated Learning Industrial Tasks, such as:
      • Automated Learning Process Optimization Task for operations
      • Automated Learning Resource Optimization Task for supply chain
  • ...
• Counter-Examples:
  • Human Learning Tasks involving experiential learning not based on algorithms.
  • Automated Inference Tasks ...
  • Deductive Reasoning Tasks focusing on logical conclusions without data-driven improvements.
  • Statistical Analysis Tasks that analyze data but may not necessarily involve learning models.
  • Exploration Tasks without the goal of predictive learning.
  • Exploitation Tasks where decisions are made without further learning.
• See: Data-Driven Prediction, Transfer Learning, Data Mining, Supervised Learning Task, Linguistically-Specified Learning Task.

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References

2024-11-22

• Perplexity.ai
  • Question: Please describe the core properties and characteristics of Automated Learning Tasks, covering: (1) Automated Learning Execution Propertys (batch vs online, static vs dynamic); (2) Automated Learning Paradigms (supervised, unsupervised, reinforcement); (3) Automated Learning Task Objectives (classification, regression, clustering); (4) Automated Learning Domain Applications (healthcare, finance, retail); and (5) Automated Learning Specialized Tasks (recommendation systems, conversational AI, predictive analytics). Include how tasks vary based on Automated Learning Data Structure Requirements (structured vs unstructured), Automated Learning Temporal Aspects (real-time vs batch), and Automated Learning Interaction Models (single-shot vs iterative). Focus on task properties rather than implementation details.
  • Answer: Automated Learning Task Propertys and characteristics can be organized into several key categories:
    • Automated Learning Paradigms:
      • Automated Learning Supervised Learning:
        • Requires Automated Learning Labeled Data with known Automated Learning Target Values
        • Used for making explicit Automated Learning Predictions from data
        • Learns from direct Automated Learning Feedback through correct Automated Learning Input-Output Pairs
      • Automated Learning Unsupervised Learning:
        • Works with Automated Learning Unlabeled Data to discover Automated Learning Hidden Patterns
        • No predefined Automated Learning Target Variables or outcomes
        • Focuses on finding natural Automated Learning Data Groupings and Automated Learning Data Relationships in data
      • Automated Learning Reinforcement Learning:
        • Learning occurs through Automated Learning Environment Interactions with an environment
        • Uses Automated Learning Reward-Automated Learning Punishment feedback mechanisms
        • Automated Learning Agent learns optimal actions through Automated Learning Trial and Error
    • Automated Learning Execution Propertys:
      • Automated Learning Mode:
        • Automated Learning Batch Learning processes complete datasets at fixed intervals
        • Automated Learning Online Learning continuously updates from Automated Learning Data Streams
        • Online learning requires more samples but is more resource-efficient
        • Batch learning allows multiple passes while online uses single-pass learning
      • Automated Learning Dynamics:
        • Automated Learning Static Learning assumes fixed Automated Learning Data Distributions
        • Automated Learning Dynamic Learning adapts to changing patterns over time
        • Automated Learning Time Series requires special handling of Automated Learning Temporal Dependencys
    • Automated Learning Task Objectives:
      • Automated Learning Classification Task:
        • Predicts Automated Learning Categorical Labels or classes
        • Can be Automated Learning Binary Classification or Automated Learning Multi-class Classification
        • Examples include Automated Learning Spam Detection, Automated Learning Sentiment Analysis
      • Automated Learning Regression Task:
        • Predicts continuous numerical values
        • Outputs are quantitative rather than categorical
        • Examples include Automated Learning Price Prediction, Automated Learning Time Series Forecasting
      • Automated Learning Clustering Task:
        • Groups similar data points together
        • Discovers natural patterns and segments in data
        • Used for Automated Learning Customer Segmentation, pattern discovery
    • Automated Learning Domain Applications:
      • Automated Learning Text Processing Task:
        • Handles variable-length sequences of words
        • Requires understanding of linguistic patterns
        • Examples include Automated Learning Sentiment Analysis, Automated Learning Translation
      • Automated Learning Sequential Data Task:
        • Deals with ordered data points
        • Requires consideration of temporal dependencies
        • Special handling needed for missing data in time series
      • Automated Learning Feature Complexity:
        • Simple features require minimal preprocessing
        • Complex features need automated extraction
        • Automated Learning Deep Learning enables automatic feature learning from raw data
    • Automated Learning Specialized Tasks:
      • Automated Learning Recommendation Task:
        • Automated Learning Content-based Recommendations match items to user preferences
        • Automated Learning Collaborative Filtering leverages patterns across users
        • Automated Learning Hybrid Recommendations combine multiple strategies
      • Automated Learning Conversational Task:
        • System-initiated vs user-initiated interactions
        • Question-answer patterns for information gathering
        • Multi-turn dialogue management with context retention
      • Automated Learning Predictive Task:
        • Cost and expenditure forecasting
        • Risk assessment and stratification
        • Performance monitoring and evaluation
        • Pattern detection in complex datasets
    • Automated Learning Task Complexitys:
      • Automated Learning Data Structure Requirement:
        • Automated Learning Structured Data Processing (tabular, time series)
        • Automated Learning Unstructured Data Processing (text, images, audio)
        • Automated Learning Multi-modal Data integration
      • Automated Learning Temporal Aspect:
        • Automated Learning Real-time Processing vs Automated Learning Batch Processing
        • Automated Learning Historical Analysis
        • Automated Learning Time Series Analysis
      • Automated Learning Interaction Model:
        • Automated Learning Single-shot Learning vs Automated Learning Iterative Learning
        • Automated Learning Interactive Feedback incorporation
        • Automated Learning Continuous Update model updating

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• • Citations:
    • [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC8372231/
    • [2] https://vitalflux.com/difference-between-online-batch-learning/
    • [3] https://www.sailthru.com/reinforcement-learning-machine-learning/
    • [4] https://neptune.ai/blog/time-series-prediction-vs-machine-learning
    • [5] https://vitalflux.com/7-common-machine-learning-tasks-related-methods/
    • [6] https://www.sciencedirect.com/topics/computer-science/machine-learning
    • [7] https://artificialintelligencemadesimple.substack.com/p/understanding-online-vs-batch-training
    • [8] https://datasciencedojo.com/blog/machine-learning-101/
    • [9] https://sunscrapers.com/blog/deep-learning-for-nlp-an-overview/
    • [10] https://accessibleai.dev/post/coremltasks/
    • [11] https://pmc.ncbi.nlm.nih.gov/articles/PMC10898280/
    • [12] https://10xds.com/blog/8-application-areas-of-machine-learning/
    • [13] https://appinventiv.com/blog/recommendation-system-machine-learning/
    • [14] https://www.leewayhertz.com/build-recommendation-system/
    • [15] https://builtin.com/artificial-intelligence/machine-learning-healthcare
    • [16] https://yongfeng.me/attach/Tutorial_on_Conversational_Recommendation_Systems.pdf
    • [17] https://iabac.org/blog/exploring-the-diverse-domains-of-machine-learning

2018

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Machine_learning Retrieved:2018-3-26.
  • Machine learning is a field of computer science that gives computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. The name Machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies. Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. Effective machine learning is difficult because finding patterns is hard and often not enough training data are available; as a result, machine-learning programs often fail to deliver.

2017

• (Yufeng G, 2017) ⇒ Yufeng G (Aug 31, 2017) "The 7 Steps of Machine Learning". In: Medium - Towards Data Science Blog.
  • QUOTE: In particular, the formula for a straight line is $y=m*x+b$, where $x$ is the input, $m$ is the slope of that line, $b$ is the $y$-intercept, and $y$ is the value of the line at the position $x$. The values we have available to us for adjusting, or “training”, are $m$ and $b$. There is no other way to affect the position of the line, since the only other variables are $x$, our input, and $y$, our output.

    In machine learning, there are many $m$’s since there may be many features. The collection of these $m$ values is usually formed into a matrix, that we will denote $W$, for the “weights” matrix. Similarly for $b$, we arrange them together and call that the biases.

    The training process involves initializing some random values for $W$ and $b$ and attempting to predict the output with those values. As you might imagine, it does pretty poorly. But we can compare our model’s predictions with the output that it should produced, and adjust the values in $W$ and $b$ such that we will have more correct predictions.

    This process then repeats. Each iteration or cycle of updating the weights and biases is called one training “step”.

    

2006

• (Mitchell, 2006) ⇒ Tom M. Mitchell. (2006). “The Discipline of Machine Learning." Machine Learning Department technical report CMU-ML-06-108, Carnegie Mellon University.
  • QUOTE: … “How can we build computer systems that automatically improve with experience, and ...”

1998

• (Mitchell, 1998) ⇒ Tom Mitchell. (1998). “?"
  • QUOTE: Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

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