Educational guide | ||||||||||||||||||||||||||||||||||||||||
IDENTIFYING DATA | 2023_24 | |||||||||||||||||||||||||||||||||||||||
Subject | FOUNDATIONS OF MACHINE LEARNING AND APPLICATIONS IN CYBERSECURITY | Code | 01747013 | |||||||||||||||||||||||||||||||||||||
Study programme |
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Descriptors | Credit. | Type | Year | Period | ||||||||||||||||||||||||||||||||||||
4 | Optional | First | First |
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Prerequisites | ||||||||||||||||||||||||||||||||||||||||
Department | MATEMATICAS |
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Coordinador |
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ncasg@unileon.es amunc@unileon.es |
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Lecturers |
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Web | http:// | |||||||||||||||||||||||||||||||||||||||
General description | ||||||||||||||||||||||||||||||||||||||||
Tribunales de Revisión |
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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 |
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To know the basic models of Machine learning and their mathematical foundations | B5730 B5731 B5736 B5737 |
C1 C2 C4 C5 |
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To implement the basic models in Python and to be able to use the corresponding libraries | B5732 B5733 B5738 B5740 |
C1 C4 |
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To know and develop Python algorithms | B5734 |
C4 |
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To know and know how to use the main metrics for the model selection | B5736 B5738 B5739 |
C4 |
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To be able to communicate their conclusions. | B5739 |
C3 |
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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 |
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 |
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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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Complementary | |
Recommendations |