COMPSCI 1090B
Data Science 2: Advanced Topics in Data Science
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transformers, language models, autoencoders, and generative models as well as basic Bayesian methods, and unsupervised learning. Students are strongly encouraged to enroll in both the fall and spring…
Topics
- multiple deep learning architectures such as CNNs
- RNNs
- transformers
- language models
- autoencoders
- and generative models as well as basic Bayesian methods
- and unsupervised learning
Topics explicitly listed in official catalogue description
Sources
- Course catalogue ↗Public 2026-2027 offerings