SM3750 - Machine Learning for Artists | ||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||
Course Aims | ||||||||||
Machine learning pervades many aspects of contemporary life. In response to this situation, media artists have also started to apply and reflect on machine learning algorithms in their own work. This course introduces basic concepts of machine learning for artists in a hands-on practical way. The focus is not on a rigorous presentation of technical material but on the use of techniques for creative purposes. The course will have two parts. First, we will introduce the fundamental concept of machine learning and other related ideas (supervised vs. unsupervised learning, regression vs. classification, etc.) and explore classical algorithms in such areas as clustering, classification, dimensionality reduction, and manifold learning. Instead of jumping directly to advanced topics like deep neural networks, we therefore begin with classical algorithms and fundamental notions to build a strong foundation. The second part will then move on to neural networks and deep learning. Students will learn how to use pretrained models and to design simple networks to perform such tasks as image classification, object recognition, semantic segmentation, depth estimation, etc. The course will mainly concentrate on practical techniques that artists can use. The focus of the course will be on image processing rather than natural language or sound, but students can develop projects in those areas on the basis of the concepts learnt in class. Students are expected to write their own code in Python and to reflect on the techniques that they use from technical, aesthetic, cultural and social standpoints. These aspects can include, for instance: a description of the history of neural networks and machine learning in relation to eugenics, cybernetics, or warfare; a discussion of the social impact of machine learning on gender, work, poverty, or race; a reflection on the philosophical aspects of machine learning, such as the nature of induction; a discussion of political and social aspects of ImageNet and other popular datasets; an awareness of the tendency of technology to become a black box and the problem of interpretable or explainable AI; questions of resource-use (for instance energy consumption, carbon emission, or impact on climate); data collection, digital labour, and surveillance capitalism; etc. Assessment will be studio-based. Workshops will be conducted using contemporary languages and frameworks, such as for instance: Python, and the main libraries are scikit-learn, scikit-image, Pytorch, or TensorFlow/Keras. The specific languages to be used will depend on the instructor. | ||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||
Continuous Assessment: 100% | ||||||||||
Detailed Course Information | ||||||||||
| SM3750.pdf | ||||||||||
Useful Links | ||||||||||
| School of Creative Media | ||||||||||
