COM SCI 267A

Probabilistic Programming and Relational Learning

Description: Lecture, four hours; outside study, eight hours. Introduction to computational models of probability and statistical models of relational data. Study of relational representations such as probabilistic databases, relational graphical models, and Markov logic networks, as well as various probabilistic programming languages. Covers their syntax and semantics, probabilistic inference problems, parameter, and structure learning algorithms, and theoretical properties of representation and inference. Expressive statistical modeling, how to formalize and reason about complex statistical assumptions and encode knowledge in machine learning models. Survey of key applications in natural language processing, graph mining, computer vision, and computational biology. Letter grading.

Units: 4.0
1 of 1
Overall Rating N/A
Easiness N/A/ 5
Clarity N/A/ 5
Workload N/A/ 5
Helpfulness N/A/ 5
1 of 1

Adblock Detected

Bruinwalk is an entirely Daily Bruin-run service brought to you for free. We hate annoying ads just as much as you do, but they help keep our lights on. We promise to keep our ads as relevant for you as possible, so please consider disabling your ad-blocking software while using this site.

Thank you for supporting us!