Digital media information is streaming in from all sorts of sensors, instruments, and simulations, overwhelming our capacity to organize, analyze, and store it. We need powerful new tools to analyze, mine, visualize, and manipulate big-media data and must rethink our whole approach to data-intensive science as the fourth scientific paradigm. In the new paradigm, we mine big data, looking for relationships and correlations, using software toolkits and programming methods to discover interesting patters and possible rules that govern them. Big data, as a data science is one of the most important sciences for the media industry and its importance is expected to continue to grow for the foreseeable future.
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Content and learning outcomes
- 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.
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
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.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- 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
Opportunity to raise an approved grade via renewed examination
- 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 about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web DM2583