COMP0169 Machine Learning for Visual Computing
Prof. Niloy J. Mitra, Tobias Ritschel
TAs, Eric-Tuan Le, Luca Morreale, Pradyumnna Reddy and Sanjeev MK
Creative industries such as print, feature films, music, fabrication or interactive media increasingly make use of multiple machine learning driven tools. This module enables students to contribute to a new shift of paradigm, where such tools become increasingly intelligent of the content being designed and the users designing them. This is enabled by machine learning, a new way of dealing with data and new forms of algorithms. We will cover an applied background of machine learning and focus on data structures particularly relevant for creative content such as images and video, and focus on learnable algorithms that allow to machines to process them intelligently, such as convolutional neural networks.
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Knowledge how to apply AI to problems from the creative industry.
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Familiarity with basic ML-based algorithms and data structures to process digital media.
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Ability of dealing with large scale data and training of machine intelligence.
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Knowing rephrasing of existing concepts from digital media with tools from AI.
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Awareness of the difficulty of computed results and artistic freedom.
| Week Date | Tentative Topic |
|---|---|
| Week 1 | Introduction to Python, Numpy and Pytorch, K-Nearest Neighbour Classifier |
| Week 2 | Optimization, First order and Second order methods |
| Week 3 | Linear and Non-linear Classification, SVM |
| Week 5 | Neural Networks |
| Week 6 | Gaussian, Laplacian filtering |
| Week 7 | Introduction to Pytorch and CNNs |
| Week 8 | CNNs filters and features |