SDSC8003 - Machine Learning | ||||||||
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| * The offering term is subject to change without prior notice | ||||||||
Course Aims | ||||||||
This course focuses on machine learning models and their deployments. Topics include neural networks, convolutional neural networks, self-attention, transformers, clustering, dimensionality reduction, autoencoder, generative adversarial networks, self-supervision, adaptation, on-device machine learning, and federated learning. | ||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||
Continuous Assessment: 70% | ||||||||
Examination: 30% | ||||||||
Examination Duration: 2 hours | ||||||||
Detailed Course Information | ||||||||
| SDSC8003.pdf | ||||||||
Useful Links | ||||||||
| School of Data Science | ||||||||
