Course - Advanced statistical methods in inference and learning - MA8701
MA8701 - Advanced statistical methods in inference and learning
Lessons are not given in the academic year 2023/2024
The course discusses principles and methods of statistical inference and learning. The topics of the course expands on the contents of the courses listed under Required previous knowledge.
1. Knowledge. Understand and explain the central theoretical aspects in statistical inference and learning. Understand and explain how to use methods from statistical inference and learning to perform a sound data analysis. Be able to evaluate strengths and weaknesses for the methods and choose between different methods in a given data analysis situation. 2. Skills. Be able to analyse a dataset using methods from statistical inference and learning in practice (using R or Python), and discuss the choices taken and the results found. 3. Competence. The students will be able to participate in scientific discussions, and read research presented in statistical journals. They will be able to participate in applied projects, and analyse data using methods from statistical inference and learning.
Learning methods and activities
Lectures, alternatively guided self-study. Practical compulsory group project in data analysis (application of course theory using R or Python) and compulsory oral group presentation of a research article/topic.
The course is usually given every second year, and only if a sufficient number of students register. If too few students register, the course is given as a guided self study.
- Mandatory work
Recommended previous knowledge
TMA4267 Linear Statistical Methods, TMA4295 Statistical inference, TMA4300 Computer intensive statistical methods, TMA4268 Statistical learning, TMA4315 Generalized linear models - or equivalent knowledge. Good understanding and experience with R, or with Python, for statistical data analysis.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics, 2009) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. In addition selected other material (chapters from books and journal articles) will be used. More detailed information will be given in the start of the course.
Credits: 7.5 SP
Study level: Doctoral degree level
Language of instruction: English, Norwegian
Department with academic responsibility
Department of Mathematical Sciences
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"