VU Hands on Machine Learning

Abstract

This course is the practical/experimental companion to the seminar on the theory and history of Machine Learning by Paul Feigelfeld (until 2023 by Clemens Apprich). While it is not a formal requirement to also attend this other course, we highly recommend to jointly attend both of those two courses. Ongoing course documentation: https://tantemalkah.at/machine-learning/

Description

The course features three major parts we will focus on: 1. Introduction to coding and data processing with Python 2. Implementation of several basic machine learning algorithms (Markov-chain based text generation, linear regression, K-Nearest-Neighbour classification, K-means-clustering, perceptrons and simple neural networks) 3. Project work, open experimentation and discussion: the final weeks of the course aim at defining specific (prototype) projects, which the attendees will work on either individually or in groups, as part of their final exercise in this course, as well as reflective discussions of the digital-material basis of "AI".

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