Educational guide
IDENTIFYING DATA 2023_24
Subject FOUNDATIONS OF MACHINE LEARNING AND APPLICATIONS IN CYBERSECURITY Code 01747013
Study programme
1753 - Máster Universitario Europeo en derecho, datos e inteligencia artificial (EMILDAI)
Descriptors Credit. Type Year Period
4 Optional First First
Language
Prerequisites
Department MATEMATICAS
Coordinador
CASTRO GARCIA, NOEMI DE
 E-mail ncasg@unileon.es
amunc@unileon.es
Lecturers
CASTRO GARCIA, NOEMI DE
MUNOZ CASTANEDA , ANGEL LUIS
Web http://
General description
Tribunales de Revisión
Tribunal titular
Cargo Departamento Profesor
Presidente MATEMATICAS GOMEZ PEREZ , JAVIER
Secretario MATEMATICAS TROBAJO DE LAS MATAS , MARIA TERESA
Vocal MATEMATICAS FRANCISCO IRIBARREN , ARACELI DE
Tribunal suplente
Cargo Departamento Profesor
Presidente MATEMATICAS HERMIDA ALONSO , JOSÉ ÁNGEL
Secretario MATEMATICAS ARIAS MOSQUERA , DANIEL
Vocal MATEMATICAS GARCIA FERNANDEZ , ROSA MARTA

Competencies
Type A Code Competences Specific
Type B Code Competences Transversal
  B5729
  B5730
  B5731
  B5732
  B5733
  B5734
  B5735
  B5736
  B5737
  B5738
  B5739
  B5740
Type C Code Competences Nuclear
  C1
  C2
  C3
  C4
  C5

Learning aims
Competences
To identify when a problem can be solve using Machine Learning techniques C1
C4
To know the basic models of Machine learning and their mathematical foundations B5730
B5731
B5736
B5737
 C1
C2
C4
C5
To implement the basic models in Python and to be able to use the corresponding libraries B5732
B5733
B5738
B5740
 C1
C4
To know and develop Python algorithms B5734
 C4
To know and know how to use the main metrics for the model selection B5736
B5738
B5739
 C4
To be able to communicate their conclusions. B5739
 C3
B5729
B5730
B5735
B5736
 C4

Contents
Topic Sub-topic
1. Introduction to Machine Learning I: Introduction to Python
II: Introduction to Machine Learning applied to Cybersecurity. Supervised learning, error and noise, Overfitting, Regularization, Validation, Introduction to unsupervised learning.
2. Introduction to Big Data III: Introduction to Big Data: introduction and technologies. Case studies.
3. Introduction to Blockchain IV: Introduction to Blockhain technology and its applications.

Planning
Methodologies  ::  Tests
  Class hours Hours outside the classroom Total hours
Assignments 1 9 10
 
Practicals using information and communication technologies (ICTs) in computer rooms 15 30 45
 
Lecture 30 15 45
 
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies   ::  
  Description
Assignments Written assignments related to the topics covered in lectures.
Practicals using information and communication technologies (ICTs) in computer rooms
Lecture Description of the contents of the subject.

Personalized attention
 
Lecture
Description
Via email or using moodle

Assessment
  Description Qualification
Assignments Written assignments related to the topics covered in lectures. 0%-60%
Practicals using information and communication technologies (ICTs) in computer rooms 0%-60%
Others Peer-review activities. Continuous assessment. 20%
 
Other comments and second call
<p>The grade will be computed attending to two types of assessment: summative and continuous. In order to pass the course, it is necessary to obtain a grade of at least 50%. The final grade will be computed only if the student has a grade of at least 40% in every assignment and test.  </p><p>For the continuous assessment, the student will need to create a portfolio, consisting on several assignments to be done along the semester, which will count for 20% of the grade. </p><p>The sumative part will consist on a written final project and two assignments, accounting for 45% of the total grade and their oral presentations, will account for 20% of the grade. </p><p>The grade of the second call will be the graded in the same way.  </p><p>At any given time, an explanation / clarification of the documents submitted by the student may be required. Students are supposed to know and accept the Regulations on Plagiarism of the University.</p><p>The use of any electronic device (cell phones, tablets, etc) allowing the student to have communication with other people will be forbidden while doing the tests, as well as any material not explicitly allowed by the professor. </p><p> If a student breaks this rule, he will fail the exam and the Academic Authority of the Center will be informed so that they can follow the procedure approved by the Governing Council of the University on January 29th, 2015.</p><div><br /></div>

Sources of information
Access to Recommended Bibliography in the Catalog ULE

Basic Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, The MIT Press,
Sebastian Raschka, Python Machine Learning, Packt Publishing Ltd,
Shai Shalev-Shwartz, Shai Ben-David, Understanding machine learning. From theory to algorithms, Cambridge University Press,
Research papers on the most relevant topics published in the last years.
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