CS109b: Advanced Topics in Data Science
Pavlos Protopapas, Mark Glickman, and Chris Tanner
Additional Instructor: Eleni Kaxiras
Advanced Topics in Data Science (CS109b) is the second half of a one-year introduction to data science. Building upon the material in Introduction to Data Science, the course introduces advanced methods for data wrangling, data visualization, statistical modeling, and prediction. Topics include big data, multiple deep learning architectures such as CNNs, RNNs, transformers, language models, autoencoders, and generative models as well as basic Bayesian methods, nonlinear statistical models, and unsupervised learning.
Lectures: Mon , Wed, & Fri 9:00‐10:15 am
Sections: Fri 10:30 am (starting 3/5)
Advanced Sections: Weds 12 pm (starting 3/10)
Office Hours: TBD
Course material can be viewed in the public GitHub repository.