109810 - BIG DATA

Subject type: Optional

Coordinator: Alfonso Palacios González

Trimester: Second term

Credits: 6

Course taught in Spanish

The aim of the course is to master analytical tools applied to Big Data environments. Understand the role of industry 4.0 (I4.0) as a new disruptor of the competitive space. This subject is essential for engineers to help them create value through the knowledge that one can extract from Big Data environments.

Most of the sessions will cover Machine Learning tools applied to I4.0 context and real cases. The process of examining large amounts of data and of a different nature to uncover hidden patterns, gain new perspectives, and possibly relevant and useful information, will be explained in detail. The course will describe in the final part some of the latest advances in Deep Learning (GAN) and Reinforcement Learning widely applied in the industry

At a general level, this subject contributes to the following learning outcomes specified for the optional subject to which it belongs.

At a more specific level, at the end of the course the student must be able to:

Ra1: Understand the role of Industry 4.0 and Big Data in revealing new knowledge.

Ra2: Prepare data to solve complex problems

Ra3: View and generate reports

Ra4: Apply analytical techniques to Supervised learning

Ra5: Understand the different Unsupervised Learning Methods.

Ra6: Evaluate Models

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.

LEFT        
1 History of data science. From Business Intelligence to Big Data    
2 Data quality and visualization. Reports and dashboards    
3 Classification    
    3.1 GLM
    3.2 Trees
    3.3 Other methods
PART I        
4 Clustering methods    
    4.1 Distance measurements
    4.2 Kmeans
    4.3 Hierarchical clustering

    4.4 Gaussian Mixture Models

    4.5 Optics

5 Association Rules
6 Text analysis    

7 Recommendation and Reinforcement Learning Systems
8 Evaluation of the model    
9 Project    

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:

PeX = Written test (Exam) [Related to all competencies]

    The test will include the contents associated with all learning outcomes

PLab = Laboratory Practices [Related to all competencies]

    The practices will allow the student to practice concepts described in theory

Evidence of learning outcomes: Ra2, Ra3, Ra4, Ra5 and Ra6

Proj = Big Data Project Presentation [Related to all skills]

    Students will present a project describing each and every one of the stages they have developed. The code, the working document and the presentation will be delivered

Evidence of learning outcomes: All

ExiP = Exercises and participation in class (presentation of the implementation of one of the algorithms explained in class) [Related to all the competences]

    Students and / or groups involved in problem solving

Evidence of learning outcomes: All

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.

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

Q = 0.25 PeX + 0.25 PLab + 0.40 Proj + 0.10 ExiP

Remarks on Recovery

The theory part of the subject and the Project (PeX and Proj) is recoverable. The other parts are not recoverable. For students who attend the recovery exam and the project delivery, their grade (PeX and / or Proj) 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 not exceed 7.

An Introduction to Statistical Learning: with Applications in R 7th Edition Gareth James and Daniela Witten

Springer Texts in Statistics - 2017

ISBN-978 1461471370

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

Decision Trees and Random Forests: A Visual Introduction For Beginners

Chris Smith and Mark Koning

Independently published (October 4, 2017)

ISBN-978 1549893759