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Presentation
Presentation
In data science, the impact of the data collection, analysis and usage in machine learning models should be properly assessed. This curricular unit introduces the concepts and considerations in ethics, security and privacy that allow to assess such impact and prevent/mitigate potential risks related with the data manipulation and storage.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 5
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Year | Nature | Language
Year | Nature | Language
3 | Optional | Português
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Code
Code
ULHT6638-24454
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
S1. Introduction to ethics. S2. Ethics and data science. S3. Data privacy: basic concepts, approaches and recommendations. S4. Legal and regulatory frameworks related to data privacy. S5. Introduction to general security concepts and their importance in machine learning systems. S6. Types of vulnerabilities in machine learning systems. S7. Mitigation approaches, regulations and security guidelines for machine learning systems.
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Objectives
Objectives
LG1. Understanding the foundations of ethics and understanding its applications in data science. LG2. Understanding the data privacy risks. LG3. Understanding the approaches and recommendations to reduce the risk of privacy leakage incidents. LG4. Understanding the legal and regulatory requirements related to data privacy. LG5. Understanding the need for specific security measures for machine learning systems. LG6. Understanding the main types of vulnerabilities on machine learning systems and the measures to prevent and mitigate them.
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Teaching methodologies and assessment
Teaching methodologies and assessment
The lectures are conducted in person and are primarily based on exposition. The content is illustrated with examples and explored by the students through small projects.
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References
References
Jarmul, K. (2023). Practical Data Privacy. O'Reilly Media, Inc. Martens, D. (2022). Data Science Ethics: Concepts, techniques, and cautionary tales. Oxford University Press.
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Office Hours
Office Hours
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Mobility
Mobility
No
