In this course, the students will learn about the relationship between technology and society, to understand the interplay between technology and social and cultural factors. Successful innovative technologies need to be adopted and accepted by the society, and be relevant to intended user groups. But what makes users choose to engage with a new technology, and what not? This course provides a fundamental basis for designing technological innovations in a socially conscious manner. What are the predominant theories that explain the socio-cultural processes behind technology? How are technologies adopted and used by different user groups? What methods can we use to learn about the user? What social and cultural perspectives should we consider when we design technologies? Why is a critical perspective important for developing innovations? The course provides insights on the adoption and use of technologies from the social and cultural perspective, to understand user engagement, involvement and diversity. The course offers an important preparation for students interested in creating social impact through the design of innovative technologies. It will offer crucial insights into the fundamental perspectives on technology and social change, provide empirical examples, and teach the most prominent practical methods to understand the role of technology for users and their everyday context.
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Content and learning outcomes
In this course, the students will learn about the relationship between data, models and algorithms, to understand how to process and draw conclusions of data through data mining and machine learning. The course introduces some theory on machine learning, but focuses mainly on current applied methods. Successful machine learning applications need to be designed through a critical engagement and understanding of data, the algorithms that can be applied based on the kind of features the data exhibits and choosing the right paradigm of machine learning. This course provides a fundamental basis for using machine learning in an ethical and responsible manner. What are the predominant paradigms in machine learning and in what situations are they best used? What perspectives should we consider when we design machine learning applications? Why is a critical perspective important for developing machine learning?
Intended learning outcomes
After passing the course, the student should have knowledge of:
- apply methods to import, combine and convert data into the appropriate format for data analysis,
- explain the benefits of data mining and be able to choose and implement appropriate methods in typical data mining use cases,
- choose, motivate, and apply standard machine learning methods and algorithms to typical use cases and present the results in appropriate ways
- design and perform performance validation of a machine learning system
- give an account on technology design, ethics and regulations when using and processing data
Literature and preparations
B.Sc. degree in engineering, social sciences or medical science (e.g. medical science or technology, engineering, statistics, applied physics, industrial management,) or similar.
Relevant documented engineering science or industrial experience that corresponds to at least a bachelor's degree.
Basic Programming Course/knowledge, in either Python or R
Swedish 3 and English 6.
Participants should bring own laptops.
Information about course literature is announced in the course PM.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- PRO1 - Project work, 7.5 credits, grading scale: A, B, C, D, E, FX, F
The examiner may apply another examination format when re-examining individual students.
The examination is split into two parts: the first part consisting of 60 percent, 4.5 ECTS of the grade and the second 40 percent, 3 ECTS.
Part 1 consists of one reaction paper, and two programmatic assignments. The programmatic assignments require working code sample and a small report explaining the code. All three are graded for 1.5 ECTS.
The second part is a project, which is again programmatic and requires a working code and a small report. This is graded for 3 ECTS.
Attendance is required for 80 percent of the lectures and seminars.
Other requirements for final grade
Approved project, assignments and participation in 80percent of the lectures.
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 CM2011