TU Wien:Similarity Modeling 1 VU (Eidenberger)

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Similarly named LVAs (Resources):

Daten[edit | edit source]

Lecturers Horst Eidenberger
Department Information Systems Engineering
When winter semester
Language English
Links tiss:188501
Master Logic and Computation Wahlmodul Knowledge Representation and Artificial Intelligence
Master Visual Computing Wahlmodul Media Understanding
Master Media and Human-Centered Computing Wahlmodul Media Understanding
Master Medizinische Informatik Wahlmodul Biosignal- und Bildverarbeitung

Inhalt[edit | edit source]

Machine learning basics with a focus on image and audio but presented in a sort of alternative way. The vocabulary and explanations used in this lecture are quite different from what is normally used in the ML-field. For example here is why the lecture is called Similarity Modelling: When you perform feature extraction/engineering you esentially build a model for your task (you select or transform some aspect of the data that is important while disregarding the rest) -> You then train a model with those extracted features -> When you then use the model to make predictions for a new input your model essentially compares how similar that new input is to the data it was trained on -> That's why it is called Similarty Modelling (even though you could also just call it plain old machine learning).

Ablauf[edit | edit source]

WT21: There are no regular lectures, just video lectures as block on Youtube (See youtube link under “Unterlagen”). You can watch the video at any time. Question hours was offered, but rarely used.

Student can choose between two examination modalities: Summarizing content from the videos or oral exam.

In the practical part, the videos of Muppet Show are presented and you should recognize certain characters like Kermit, Pigs, etc. using ‘classic’ Machine Learning and Deep Learning both via audio and images.

Benötigte/Empfehlenswerte Vorkenntnisse[edit | edit source]

WT21 Some knowledge of ML/DL is very helpful.

WT22/23 : You definetly need prior knowledge in machine learning and deep learning, while it technically is taught in the lecutre, the explanations there are not enough for someone starting from zero. It also helps to have some knowledge on how to work with image / audio data (e.g what a spectrogram is / how they are created)

Vortrag[edit | edit source]

There are no slides, the learning materials are videos where Prof. Eidenberger talks while drawing on a sheet of papers.

Übungen[edit | edit source]

noch offen

Prüfung, Benotung[edit | edit source]

noch offen

Dauer der Zeugnisausstellung[edit | edit source]

noch offen

Zeitaufwand[edit | edit source]

WT21: The Lab Course requires some work, but is all within the 3 ECTS

Unterlagen[edit | edit source]

WT21: Lectures and Lab Course: https://www.youtube.com/playlist?list=PLmphOgUcAYnS7o0ys4Tj-Q7iEXHUQpkd3

Tipps[edit | edit source]

WT21: It makes sense to do SIM1 and SIM2 both together, but you can also do SIM1 only.

Verbesserungsvorschläge / Kritik[edit | edit source]


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