Statistical Learning for Data Science
The process of learning models and inferring quantities from data lies at the heart of several fields of study, including machine learning, signal processing, control theory, and statistics. The goal of this PhD-level course is to provide a solid statistical foundation for researchers in related fields.
We will tackle certain important issues in parameter inference and policy evaluation that are not adequately addressed in conventional machine learning and statistics textbooks.
Course schedule
| Lecture | Date | Room | Topics | Suppl. reading |
|---|---|---|---|---|
| Fundamental concepts | Sept 8 | |||
| Inference and model learning | ||||
| Validation, regularization, Bayesian approach | ||||
| Recursive methods, adaptation, time series data | ||||
| Policies and causal structures | ||||
| Predictive policies | ||||
| Interventional policies, summary |
Examination
- Weekly homeworks (7hp)
- (Optional project corresponding to a three-page research paper (+3hp))
Prerequisites
Undergraduate courses in linear algebra and probability theory.
Registration
E-mail Dave Zachariah.