Skip to main content
Department of Information Technology

Statistical Learning and Inference for Data Science (9+3hp)

September 2020

fourspaces.png

Description

The process of learning models and inferring quantities lies at the heart of data science, including machine learning, signal processing and statistics. The goal of this graduate-level course is to provide a solid statistical foundation for researchers in data science. The course will tackle certain important issues that are not adequately addressed in conventional machine learning and statistics textbooks.

Course schedule

Lec. Date Topics Background reading
L1 2020-09-24 @ 10.15 Fundamental concepts in statistical learning (W: 1, 2, 3) W: 6, 4, 5.
L2 2020-10-01 @ 10.15 Confidence sets, model learning W: 6.3.2, 8, 9
L3 2020-10-15 @ 10.15 Model validation, Bayesian regularization, W: 11
L4 2020-10-22 @ 10.15 Robust learning, Regression W: 13, 14
L5 2020-11-12 @ 10.15 Classification W: 22, 10
L6 2020-11-19 @ 10.15 Causal structures of DGPs W: 16, 17
L7 2020-11-26 @ 10.15 Causal inference TBA
L8 2020-12-03 @ 10.15 Summary TBA

Background readings (W) from Larry Wasserman's book All of Statistics.

Examination

  • Weekly homeworks (9hp)
  • Voluntary project corresponding to a three-page research paper (3hp)

Prerequisites

Undergraduate courses in linear algebra and probability theory.

Registration

E-mail the course responsible.

Teacher & Course Responsible

Dave Zachariah

Updated  2021-02-25 15:24:45 by Dave Zachariah.