Introduction to ALS

Introduction

Lecture

1

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

2

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

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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

4

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

5

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 Intro

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