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Autonomous Learning Systems: Transitioning Data Streams to Real-Time Knowledge by Plamen Angelov

This introductory lecture outlines Autonomous Learning Systems (ALS), a crucial framework for understanding how to transform data streams into actionable knowledge in real time. Accompanying the book on ALS, these notes serve as a valuable resource for courses on Machine Learning, Data Mining, System Engineering, and more. Covering topics such as probability theory, pattern recognition, fuzzy systems, and evolving user behavior modeling, this lecture series includes both theoretical foundations and practical applications to enhance the learning experience.

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Autonomous Learning Systems: Transitioning Data Streams to Real-Time Knowledge by Plamen Angelov

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  1. Autonomous Learning Systems: • From Data Streams to Knowledge in Real Time • by Plamen Angelov Introduction Lecture 1

  2. Intro to ALS • These lecture notes accompany the book on ALS • They can be used with the book and the software for courses on ALS and related subjects of: • Machine learning (ML); • System engineering (specifically, system identification), SI; • Data mining, DM; • Statistical Analysis, SA; • Pattern Recognition including clustering, classification, PR; • Fuzzy logic and fuzzy systems, including neuro-fuzzy systems, FL; ALS by P Angelov (c) 2013 Wiley LN1 Intro

  3. Outline of the ALS course • 20-30h lectures • 20 h practical session using the EST software • Lectures cover: • Introduction to ALS (1h) • Probability theory-basics (2h) • Machine Learning & Pattern recognition (2h) • Fuzzy Systems Theory – basics (2h) • Evolving System Structure (2h) • Parameters Learning (1h) • Autonomous Predictors/Estimators/Filters/ Inferential Sensors (1h)… ALS by P Angelov (c) 2013 Wiley LN1 Intro

  4. Outline of the ALS course … • … • AL Classifiers (1h) • AL Controllers (1h) • Collaborative ALS (1h) • AL Sensors in (Petro-) Chemical industry (1h) • ALS in Mobile Robotics (1h) • Autonomous Novelty Detection (1h) • Modelling Evolving user behaviour with ALS (1h) • Review (1h) • Tutorial (1h) ALS by P Angelov (c) 2013 Wiley LN1 Intro

  5. Outline of Lecture1 • Introduction to ALS • Autonomous Systems (AS) • The role of Machine Learning (ML) in AS • System Identification – A World Model • System structure identification • Parameter identification • Novelty detection, outliers, structure identification • Online vs Offline • Adaptive and Evolving • Evolving vs Evolutionary • Supervised vs unsupervised learning ALS by P Angelov (c) 2013 Wiley LN1 Intro

  6. DM SA SI ALS PR ML FL Introduction to ALS • ALS interacts with a number of well established areas of research ( a non exhaustive list is shown below): ALS by P Angelov (c) 2013 Wiley LN1 Intro

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