General information


Subject type: Optional

Coordinator: Julián Horrillo Tello

Trimester: Second term

Credits: 6

Teaching staff: 

Xavier Font Aragonés

Teaching languages


Course taught in English

Skills


Transversal competences
  • CT1: That the students know a third language, which will be preferably English, with an adequate level of oral and written form and in accordance with the needs of the graduates in each degree.

  • CT2: That students have the ability to work as members of an interdisciplinary team either as one more member, or performing management tasks in order to contribute to developing projects with pragmatism and a sense of responsibility, assuming commitments taking into account count available resources.

Description


Optional subject framed in the block of the mention in Intelligent Manufacturing in Industry 4.0.

Its goal is to help the student master the basics of Big Data, understand the importance of data quality, and get introduced to the use of analytical tools applied to big data environments.

The course describes the process of examining and processing large amounts of data and of a different nature to discover hidden patterns, gain new perspectives, and how to visualize the results obtained. It presents some of the most used advances today grouped in what is called Deep Learning and presents the use of digital twin environments.

 

Learning outcomes


At the end of the course the student must be able to:

  • LO1: Know how to use the CRISP-DM framework in data projects and understand the role of industry 4.0 and Big Data to reveal new knowledge (CE 3).
  • LO2: Prepare data to solve complex problems (CE 3.
  • LO3: View and generate reports (CE 3).
  • LO4: Apply analytical techniques to supervised learning (CE 3).
  • LO5: Understand the different methods of unsupervised learning (CE 3).
  • LO6: Evaluate models (CE3).

 

Working methodology


All the theoretical concepts of the subject will be exposed in theory classes (large groups) and / or in laboratory sessions (small groups). In these classes, and at the discretion of the teachers, exercises and problems of a more practical nature will also be solved. Likewise and always at the discretion of the teachers, students may be asked to solve, individually or in groups, short problems and / or exercises. These activities, which due to their optional nature and brevity, will serve the student as an instrument of self-assessment of their achievement of the contents of the subject and can be used by the teacher to assess it.

 The more practical concepts and everything that can essentially be considered the practical application of the theoretical concepts will be worked on in small (laboratory) groups. In the sessions scheduled for this purpose, the appropriate tools will be given to solve the scheduled activities. Sometimes students will have to complete them during the time of autonomous learning. Whenever deemed appropriate, totally optional activities will be made available to students to help them prepare and prepare for the compulsory ones.

 

Contents


Contents

 

Content title 1: Introduction to Big Data

Dedication:  

Large Group: 4

Small Group: 2

Autonomous learning: 9

Description

  • Introduction to the CRISP-DM framework
  • Industry 4.0 and smart manufacturing
  • Introduction to Big Data

Related activities

Active1, Active 2 and Active 3

       

 

Content title 2: Data Preparation

Dedication:  

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Data preparation
  • Data quality
  • Detection of extreme and atypical values
  • Reduction of dimensionality
    • PCA
    • MDS

Related activities

Active1, Active 2, Act3 and Active 4

       

 

Content title 3: Supervised Learning Methods

Dedication:  

Large Group: 12

Small Group: 6

Autonomous learning: 27

Description

  • Introduction and cases
  • GLM / How to evaluate performance
  • XGBoost
  • Deep Learning
  • Case study

Related activities

Active1, Active 2, Act3 and Active 4

       

 

Content title 4: Unsupervised Learning Methods

Dedication: 

Large Group: 8

Small Group: 4

Autonomous learning: 18

Description

  • Introduction
  • Hierarchical methods
  • K-means
  • Spectral Clustering
  • DBSCAN
  • Case study

Related activities

Active1, Active 2, Act3 and Active 4

       

 

Content title 5Display of results

Dedication: 

Large Group: 4

Small Group: 2

Autonomous learning: 9

Description

  • Introduction to visualization
  • Reporting tools and methods
  • Data presentation

Related activities

Active 2, Act3 and Active 4

       

 

 

 

 

Content title 6: Advanced Methods

Dedication: 

Large Group: 4

Small Group: 2

Autonomous learning: 9

Description

  • Deep Learning applications
    • GAN
  • Reinforcement learning
  • Predictive Maintenance
  • digital twin

Related activities

Active2, Active 3 and Active 4

Learning activities


A series of activities of an eminently practical nature (short exercises, problems ...) are made available to students, which are the basis of the learning activities of the subject. These activities will have to be solved by the students, often in a non-contact way, following the instructions of the teachers and will also be worked in class, either as examples in the theory sessions or in the laboratory sessions. Although these activities will be optional (teachers will not individually verify the performance by students), they will be essential to achieve the theoretical and practical knowledge of the subject.

 

In order to gather evidence of the achievement of the expected learning outcomes, the following evaluative activities are carried out:

 

Title of the activity 1: Written test (Pex1) 25%

 

Dedication:

Large Group:

Small Group:  

Autonomous learning:   

General description

 The test will include the contents associated with all learning outcomes

Support material

 It will be provided through the ecampus or in class

Skills

 [Related to competences CE3, CB5 and CT1]

Deliverables and links to the evaluation

 Weight within the evaluation: 25%

Specific objectives

 Evaluate units 1-6

       

 

Title of the activity 2: Project (Proj) 35%

Dedication: 

Large Group:

Small Group:   

Autonomous learning:    

General description

Completion of a data processing project showing some of the different techniques seen in the course. (RA1: RA6)

Support material

 It will be provided through the ecampus or in class

Skills

 [Related to competences CE3, CB5 and CT1]

Deliverable and links to the evaluation

 Weight within the evaluation: 35%

Specific objectives

Assess competency achievement and learning outcomes

       

 

Title of the activity 3: Exercises and Participation (ExiPar) 15%

Dedication: 

Large Group:

Small Group:   

Autonomous learning:   

General description

Students and / or groups involved in problem solving will include the contents associated with all learning outcomes.

Support material

 It will be provided in class and / or through the ecampus or in class

Skills

 [Related to all skills]

Deliverable and links to the evaluation

 Weight within the evaluation: 15%

Specific objectives

 Implement problem solving

       

 

Title of the activity 4: Practices (Lab) 25%

Dedication: 

Large Group:     

Small Group:   

Autonomous learning:    

General description

Laboratory Practices [Related to all competencies]

Support material

It will be provided in class and / or through the ecampus

Skills

[Related to all skills]

Deliverable and links to the evaluation

Weight within the evaluation: 25%

Specific objectives

The practices will allow the student to understand a problem that involves its resolution using computer solutions.

       

 

 

Evaluation system


ACTIVITIES

PES discipline

EXAMS

Pex1 25%

EXERCISES

ExiPar 15%

WORK EXPERIENCE

lab 25%

PROJECT

Project 35%

 

 

 

 

 

 

 

 

The final grade is the weighted sum of the grades for the learning activities:

Q = 0.25 Pex1 + 0.35 Proj + 0.15 ExiPar + 0.25 Lab

Remarks on Recovery

The theory part of the Pex1 subject is recoverable as well as the Project part. The other parts are not recoverable. For students attending the resit exam their Pex1 grade will be the one obtained in this test and their final grade (Q) will be calculated with the formulas detailed above and in no case will it exceed 7.

Rules for carrying out the activities

Remarks:

To pass the assessment activities, students must demonstrate the MECES Level - 2:

• (point c) have the ability to collect and interpret data and information on which to base their conclusions, including, where necessary and relevant, reflection on issues of a social, scientific or ethical nature in the field of their field of study

• (point e) know how to communicate to all types of audiences (specialized or not) in a clear and precise way, knowledge, methodologies, ideas, problems and solutions in the field of their field of study;

• (point f) be able to identify their own training needs in their field of study and work or professional environment and to organize their own learning with a high degree of autonomy in all types of contexts

For each activity, teachers will be informed of the particular rules and conditions that govern them

The one-to-one activities presuppose the student's commitment to carry them out individually and without any collaboration with other people. All activities in which the student does not comply with this commitment to individuality will be considered suspended (grade 0), regardless of their role (sender or receiver) and without this excluding the possible application of other sanctions in accordance. with the current Disciplinary Regime.

 

Likewise, the activities to be carried out in groups presuppose the commitment on the part of the students who make it up to carry them out within the group and without any kind of collaboration with other groups or people who are alien (group individuality). All activities in which the group has not respected this commitment regardless of its role (sender or receiver) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime will be considered suspended (rating 0).

In the case of activities that can be done in groups, when in any of them the commitment of group individuality is not respected and / or fraudulent means are used in its accomplishment, the qualification of the activity will be, for all members of the group, of 0 points (Activity Note = 0) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime.

 

Any undelivered activity will be considered scored with zero points

It is optional for teachers to accept or not deliveries outside the deadlines indicated. In the event that these late deliveries are accepted, it is up to the teacher to decide whether to apply a penalty and the amount of this.

REFERENCES


Basic

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) 1st Edition John D. Kelleher, Brian Mac Namee and Aoife D'Arcy

The MIT Press; 1 edition July - 2015

ISBN-978 0262029445

Complementary

Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI 1st Edition Darren Cook

O'Reilly Media; 1 edition, December 2016

ISBN-978 1491964606

Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R. by Nataraj Dasgupta (Packt Publishing; 1st Ed - 2018)