Machine Learning

Clemens Apprich
Institute of Fine Arts and Media Art, Media Theory
2023S, scientific seminar (SEW), 4.0 ECTS, 2.0 semester hours, course number S04311

Description

“Whatever the levels of abstraction associated with machine learning, the code is hardly ever hermetically opaque. As statements, everything lies on the surface” (Adrien Mackenzie).  

Everyone talks about Machine Learning, but what exactly does it mean? As a subfield of artificial intelligence, the question of whether or not machines are able to learn from experience goes back to the early days of computing (e.g. Alan M. Turing). Whereas in the 1950s and 1960s the field was dominated by symbolic-cognitive forms of learning, the focus has shifted towards a new paradigm in recent years: connectionist approaches, exemplified by artificial neural networks. These kinds of algorithms transform not only the way machines are programmed, but arguably also our understanding of programming itself. In this context, machine learning is often regarded as a powerful instrument with opaque inner processes. However, the code itself as well as the documentation and infrastructures around it are – in most cases – freely available and can, therefore, be made subject to critical inquiry. 

This course takes a media theoretical as well as historical look at machine learning. As such it is conceptualized in direct dialogue with the Hands on Machine Learning course conducted by Andrea Klaura. While the latter will allow you to get your hands dirty with actual machine learning models, this course will help you to critically reflect on some of the main principles, logics, and actual techniques behind those models. We, therefore, highly recommend to attend both courses. By combining a hands-on approach with a ‘critical close reading’ of machine-based learning algorithms, the goal is to merge a historical perspective on machine learning (Turing, Rosenblatt) with more recent critiques of the field (Amaro, Crawford, Mackenzie).

Examination Modalities

Active Participation (30%): At the core of this course lies the joint discussion of the texts, and therefore your presence and involvement are required. The class activity will center on close readings of texts and debates around the arguments forwarded in them. In order to facilitate productive in-class discussions between, you are asked to read and work through the assigned texts beforehand.  

Research/Reflection Paper (70%): Students will be responsible for a final research paper due at the end of the term (for deadlines see the outline). The research paper should be similar in scope and format to a scholarly conference paper (ca. 20.000 characters, including spaces and footnotes, but excluding appendices, references, or figures) based on at least three references from the seminar literature as well as at least three external references (you can, of course, also use more references). In case students are also attending the course Hands on Machine Learning, they can alternatively write a shorter reflection paper (10.000 characters) on their final projects (for details see the course description).

Comments

Required readings as well as a detailed course outline will be made available via the cloud. The working language will be English. Research/Reflection papers can be submitted in English and German.

Key Words

medientheorie, media theory, machine learning, artificial intelligence, tensorflow

Dates

01 March 2023, 14:30–16:00 Seminar Room 33 , "Preliminary Session"
08 March 2023, 14:30–16:00 Seminar Room 33
22 March 2023, 14:30–16:00 Seminar Room 33
29 March 2023, 14:30–16:00 Seminar Room 33
19 April 2023, 14:30–16:00 Seminar Room 33
26 April 2023, 14:30–16:00 Seminar Room 33
03 May 2023, 14:30–16:00 Seminar Room 33
10 May 2023, 14:30–16:00 Seminar Room 33
17 May 2023, 14:30–16:00 Seminar Room 33
24 May 2023, 14:30–16:00 Seminar Room 33
31 May 2023, 14:30–16:00 Seminar Room 33
07 June 2023, 14:30–16:00 Seminar Room 33
14 June 2023, 14:30–16:00 Seminar Room 33
21 June 2023, 14:30–16:00 Seminar Room 33

Course Enrolment

Via online registration

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