2. Introduction Lecture 1

3. 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 Intro2

4. 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 Intro3

5. 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 Intro4

6. 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 Intro5

7. Introduction to ALS  ALS interacts with a number of well established areas of research ( a non exhaustive list is shown below): ML SI DM SA PR FL ALS ALS by P Angelov (c) 2013 Wiley LN1 Intro6