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

This is a phd course in statistics - and requires active participation. Teaching activities are physical, except some of the Part 4 activities.

Course coordinator and lecturer: Mette Langaas
Lecturer: Kjersti Aas (Part 4) on explainable AI/interpretable machine learning

Scheduled teaching:

• Monday and Friday 10.15-12.00 in S21 (Sentralbygg 2, across from Smia), Gløshaugen.
• This will be active teaching with discussions and student contributions.
• Office hours 9-10 before class on Mondays and Fridays in Mettes office, 1236, Sentralbygg 2

Course parts:

• Part 1: Core concepts
• Part 2: Shrinkage and regularization
• Part 3: Ensembles (of trees and other methods)
• Part 4: Explainable AI
• Part 5: Closing

Evaluation

The grade for this course is pass/fail evaluated with an individual oral exam.

To attend the exam you need to have done the following activities - all in groups (size 2-3):

• Data analysis group project: data analysis short report with peer review followed by evaluation by lecturer
• Oral presentation of chosen/assigned scientific article or manuscript

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.

• Philip Stanley Mostert
• Jacob Daniel Benestad
• Didrik Lindløv Sand

First meeting Friday 27.01 12.15, second meeting 20.03 12.15, last meeting 25.05 kl 10.15. Minutes from the meeting uploaded at Blackboard.