MA8701 Advanced statistical methods in inference and learning - Spring 2021

This is a phd course in statistics - and requires active participation. Zoom-link to activity sent by email to participants.

Course coordinator/lecturer: Mette Langaas
Guest lecturers:

  1. Benjamin Dunn (in L3) on algorithms for lasso and lasso friends
  2. Berent Å. S. Lunde (L6, 15.02) on boosting tre ensembles and hyperparameter tuning
  3. Samia Touileb and Jeremy Barnes: Analysing text with neural networks (L10, 15.03)
  4. Kristian Gundersen: Uncertainty in neural networks with Bayesian neural networks and Variational infence (L11, 22.03)
  5. Kjersti Aas (Part 4, L12+L13) on explainable AI/interpretable machine leaning

Scheduled teaching: Lectures are Mondays at 9.15-12.00 (zoom). On Monday 01.03, 08.03 and 26.04 we were hybrid in S21 and zoom.

Mette has office hours in 1236 and on zoom on Wednesdays at 14.15-15.00.

The same zoom link is used for both lectures and office hours.

Formal NTNU course description: https://www.ntnu.edu/studies/courses/MA8701#tab=omEmnet

Graphical overview of the course (click on the image for higher resolution version).

Course parts:

  • Part 1: Shinkage
  • Part 2: Ensembles
  • Part 3: Neural nets
  • Part 4: Explainable AI

Grading elements:

The grade for this course is pass/fail (has "always" been this way), and 70/100 score is required to pass each of the two elements: portfolio and final oral exam. The pass/fail at 70/100 is the standard rule for PhD courses at NTNU)

Evaluation plan (approved by IE faculty 17.02.2021)

1) Portfolio [75%] consisting of three element that will count equally:

  • Project 1 (Part 1 topics) data analysis short report (in group of size 1-3) with peer review followed by evaluation by lecturer [25%].
  • Project 2 (Part 2-4 topics) data analysis short report (in group of size 1-3) [25%].
  • Oral presentation of chosen/assigned scientific article or manuscript (in group of size 1-3) [25%].

2) Individual oral exam [25%]

Course prerequisites:

  • TMA4267 Linear statistical models
  • TMA4295 Statistical inference
  • TMA4300 Computer intensive statistical methods,
  • TMA4315 Generalized linear models
  • TMA4268 Statistical learning

Helpful knowledge

  • TMA4180 Optimization

Programming/IT-knowledge

Good programming skills in either R or Python.

Reference group

Dates for meetings with reference group: 28.01, 11.03, 29.04.

2021-04-25, Mette Langaas