Advanced Topics in Data Science

Building upon the material in CSCI E-109a , the course introduces advanced methods for statistical modeling, representation, and prediction.

Topics include multiple deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (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 course within the same academic year.

Students who have previously completed CSCI E-107 or CSCI E-109 may not take CSCI E-109a or CSCI E-109b for degree or certificate credit.