DSC 140BRepresentation Learning
This course is an introduction to machine learning which explores techniques for learning suitable representations from data. Topics include clustering, dimensionality reduction, manifold learning, principal component analysis, spectral embeddings, multilayer perceptrons, autoencoders, convolutional and recurrent neural networks, and other aspects of deep learning. The course focuses on the underlying mathematical principles, but some attention is also given to implementation.
Prerequisites
From the catalog: “DSC 80/R and (ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 183 or MATH 186). Restricted to students within the DS25 major. All other students will be allowed as space permits.”
Where it sits in degree plans
Suggested plans most often put it in Year 3 · Winter (1 program).
One of the options in (1)
- Data Science (B.S.)