- Basic methods: pattern recognition, machine learning, data analysis, data visualization, network analysis, and commonly used tools for data mining and visualization of big data.
- Case studies (seminar): students study selected cases and use course methods for analysis and data visualization.
- Small student projects: the students use big data methods for, e.g., studying consumer media use and purchase behaviours. Will be presented as a short research report.
DM2583 Big Data in Media Technology 7.5 credits
Welcome to "Big Data in Media Technology," where you will explore the integration of advanced AI, particularly generative artificial intelligence, into the handling and analysis of large datasets in digital media. This course will equip you with the skills to harness AI tools like convolutional neural networks, transformers, and large language models, such as ChatGPT, transforming media production landscape, content creation, and data analysis.
During this course, you will:
- Understand the application of big data techniques within the media sector, using AI to uncover patterns and enhance decision-making.
- Dive into the capabilities of generative AI to innovate in content creation and media personalization.
- Discuss the ethical considerations and management of AI technologies to ensure their responsible usage
By engaging with practical projects and case studies, you will gain hands-on experience that prepares you for the evolving demands of technology in media. Equip yourself with the knowledge to lead in a data-driven media environment, where AI's potential to innovate is boundless.
Join us to redefine the intersection of big data technology and media with AI-driven tools and insights.
Choose semester and course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Application
For course offering
Autumn 2024 bdata programme students
Application code
50326
Headings with content from the Course syllabus DM2583 (Spring 2020–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
Having passed the course, the student should be able to
- account for basic methods, technologies and tools in big data analysis
- use scientific big data technologies, tools and methods to solve practical problems in media technology,
- perform the most important stages in big data work from collecting, preparing and modelling data to evaluation and dissemination of results
- explain important machine learning concepts such as feature extraction, cross validation, generalisation and over fitting, prediction and the curse of dimensionality
- account for how common data modelling methods work, their applications, and describe their assumptions and limitations
- apply common data modelling frameworks, technologies and tools within a broad spectrum of media application areas
- apply and evaluate results derived from use of common data modelling frameworks, by means of Matlab or Python
Literature and preparations
Specific prerequisites
Degree of Bachelor or the equivalent. SF1604 Linear Algebra, SF1625 One variable calculus, SF1626 Multivariable analysis, SF1901 Probability and Statistics or the equivalent. Basic knowledge in Matlab or Python.
Recommended prerequisites
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Equipment
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Literature
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Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- LIT1 - Literature, 2.5 credits, grading scale: P, F
- PRO1 - Project, 5.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
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Opportunity to raise an approved grade via renewed examination
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Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Computer Science and Engineering
Education cycle
Second cycle
Add-on studies
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