Educational guide
IDENTIFYING DATA 2020_21
Subject INTELLIGENT SYSTEMS IN INDUSTRY Code 01744004
Study programme
1744 - MASTER UNIVERSITARIO EN INDUSTRIA 4.0
Descriptors Credit. Type Year Period
3 Compulsory First First
Language
Castellano
Prerequisites
Department ING.ELECTR.DE SIST. Y AUTOMATI
Coordinador
ALEGRE GUTIÉRREZ , ENRIQUE
E-mail ealeg@unileon.es
arods@unileon.es
Lecturers
ALEGRE GUTIÉRREZ , ENRIQUE
RODRÍGUEZ DE SOTO , ADOLFO
Web http://
General description The course introduces the student to the current use being made of artificial intelligence in the industry to create intelligent systems that contribute to the development of Industry 4.0. Initially, some of the fundamental concepts and methods of artificial intelligence are introduced and revised, focusing on those techniques that have a greater application in the industry. Later, some of the main techniques used in the creation of intelligent systems are presented, such as rule systems, probabilistic methods and deep learning. Finally, the main use cases in different types of industry are explained, presenting examples of successful applications and providing criteria for using artificial intelligence in real applications. The theoretical part is complemented by various practices carried out using the Python language, where the student will be able to see the application of some of the methods explained in theory to solve industrial problems.
Tribunales de Revisión
Tribunal titular
Cargo Departamento Profesor
Presidente ING.ELECTR.DE SIST. Y AUTOMATI ALAIZ MORETON , HECTOR
Secretario ING.ELECTR.DE SIST. Y AUTOMATI ALAIZ RODRIGUEZ , ROCIO
Vocal ING.ELECTR.DE SIST. Y AUTOMATI ALONSO CASTRO , SERAFIN
Tribunal suplente
Cargo Departamento Profesor
Presidente ING.ELECTR.DE SIST. Y AUTOMATI DOMINGUEZ GONZALEZ , MANUEL
Secretario ING.ELECTR.DE SIST. Y AUTOMATI RODRIGUEZ SEDANO , FRANCISCO JESUS
Vocal ING.ELECTR.DE SIST. Y AUTOMATI BENAVIDES CUELLAR , MARIA DEL CARMEN

Competencies
Type A Code Competences Specific
  A18640
  A18641
Type B Code Competences Transversal
  B5708
  B5711
  B5712
  B5715
  B5716
Type C Code Competences Nuclear
  C1
  C3

Learning aims
Competences
The student will acquire theoretical knowledge about methods based on machine learning, will be able to explain their foundations and will know how to apply them in an industrial process. The student will also be able to develop simple intelligent systems using a high-level programming language and will understand the different stages involved in developing an intelligent system that is applied to an industrial problem. A18640
A18641
 B5708
B5711
B5712
B5715
B5716
 C1
C3

Contents
Topic Sub-topic
Block I: INTRODUCTION TO INTELLIGENT SYSTEMS Lesson 1: INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Fundamentals of AI, smart agents, basic methods, state and search spaces, knowledge representation and reasoning.

Lesson 2: UNCERTAINTY AND ARTIFICIAL INTELLIGENCE
Representation of uncertainty, probability, fuzzy sets, belief functions, Markov models, Kalman filters.

Lesson 3: MACHINE LEARNING
Introduction to machine learning. Supervised learning. Models for learning: decision trees, linear models, neural networks, support vector machines. Unsupervised learning.

Lesson 4: ARTIFICIAL VISION AND NATURAL LANGUAGE PROCESSING
Introduction and basic concepts of Machine Vision. Example of a task in VA: Image Classification. Definition, main stages and algorithms used. Natural language processing, concept Example of a task in NLP: Text Classification. Definition, main stages and algorithms used.
Block II: INTELLIGENT SYSTEMS AND THEIR APPLICATIONS IN INDUSTRIAL PROCESSES Lesson 1: RULES-BASED AND INTELLIGENT CONTROL SYSTEMS
Classic rule systems, forward and backward search, fuzzy rule systems and intelligent control

Lesson 2: PROBABILISTIC METHODS
Statistical learning, Probabilistic graphical methods

Topic 3: NEURAL NETWORKS AND DEEP LEARNING
Basic models of neural networks, deep learning in neural networks, CNNs, autoencoders.
Block III: USE CASES IN DIFFERENT TYPES OF INDUSTRY AND THEIR RELATION TO METHODS BASED ON ARTIFICIAL INTELLIGENCE Lesson 1: INTELLIGENT SYSTEMS IN INDUSTRY
Criteria and main areas of application of intelligent systems in the industry

Lesson 2: APPLICATIONS
Examples of successful applications of intelligent systems in the industry.
Block IV: DEVELOPMENT OF APPLICATIONS BASED ON MACHINE LEARNING FOR THE SMART INDUSTRY Lab 1: INTRODUCTION TO PYTHON
Installation and configuration of a programming environment with Python language. Top python libraries for machine learning

Labs 2, 3, 4: TROUBLESHOOTING IN INDUSTRIAL APPLICATIONS
Guided implementation of technical applications of NLP, CV and Machine Learning to solve real problems in the industry.

Planning
Methodologies  ::  Tests
  Class hours Hours outside the classroom Total hours
Practicals using information and communication technologies (ICTs) in computer rooms 8 20 28
 
 
Lecture 15 21 36
 
Mixed tests 1 10 11
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies   ::  
  Description
Practicals using information and communication technologies (ICTs) in computer rooms Use of free software, Python, to carry out guided practices that will allow the student to make small applications that help solving problems in the industry, applying artificial intelligence techniques.
Lecture Theoretical presentations in the classroom, containing examples with use cases, applications and exercises.

Personalized attention
 
Lecture
Practicals using information and communication technologies (ICTs) in computer rooms
Description
The student will request tutorials with the teacher, either in person, by email or through a forum arranged for this purpose.

The tutorials may be in person or, if necessary, using videoconferencing tools.

Assessment
  Description Qualification
Lecture Written exams to assess the assimilation of the concepts taught. 35%
Practicals using information and communication technologies (ICTs) in computer rooms They will be evaluated through deliveries made by students 30%
Others Other activities that will allow continuous assessment of the content taught. 35%
 
Other comments and second call

In the second call, the criteria applied will be the same followed in the first one.


ADDENDUM
Contingency plan due to COVID-19 emergency conditions that prevents from presence based teaching
COVID-19 Teaching Guide Addendum Access Link


Sources of information
Access to Recommended Bibliography in the Catalog ULE

Basic
Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer; Edición: 1st ed. 2006. Corr. 2nd printing 2011.

Richard Duda & Peter Hart. Pattern Classidication. A. Wiley-Interscience. 2000. 

Richard Szeliski. Computer Vision: Algorithms and Applications. Springer. 2010.

G. Cheng, L. Liu, X. Qiang and Y. Liu, "Industry 4.0 Development and Application of Intelligent Manufacturing," 2016 International Conference on Information System and Artificial Intelligence (ISAI), Hong Kong, 2016, pp. 407-410, doi: 10.1109/ISAI.2016.0092.

Dufek, D., Ignas, T. & Strandberg, F. (2019). Sistemas Inteligentes en la Industria 4.0. Revista Antioqueña de las Ciencias Computacionales y la Ingeniería de Software (RACCIS), 9(2), 43-48.

Ray Y. Zhong, Xun Xu, Eberhard Klotz, Stephen T. Newman, Intelligent Manufacturing in the Context of Industry 4.0: A Review,

Engineering, Volume 3, Issue 5, 2017, Pages 616-630.

 

Jay Lee, Hossein Davari, Jaskaran Singh, Vibhor Pandhare. Industrial Artificial Intelligence for industry 4.0-based manufacturing systems,

Manufacturing Letters. Volume 18. 2018. Pages 20-23.

 

Alex Castrounis. AI for People and Business: A Framework for Better Human Experiences and Business Success. O'Reilly Media. 2019

Andrew Park. Machine Learning: This Book Includes: Python Machine Learning and Data Science. A Comprehensive Guide for Beginners to Master Deep Learning, Artificial Intelligence and Data Science with Python. Independently published. 2020.
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