course-details-portlet

MA8701 - Advanced statistical methods in inference and learning

About

Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Passed/Failed

Evaluation form Weighting Duration Examination aids Grade deviation
work 30/100 ALLE
Oral examination 70/100 D

Course content

The course is usually given every second year, and only if a sufficient number of students register. It is given next time Spring 2021. If too few students register, the course is given as a guided self study.

The course provides a broad introduction to the basic principles and methods of statistical inference and prediction. Together with courses "MA8704 Probability theory and asymptotic techniques" and "MA8702 Advanced modern statistical methods" it provides a theoretical basis for PhD students in statistics.

The topics of the course expands on the contents of the courses listed under Required previous knowledge.

Learning outcome

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 give a good presentation and discussion of the choices done and the results found.

3. Competence
The students will be able to participate in scientific discussions, read research presented in statistical journals, and carry out research in statistics at high international level. They will be able to participate in applied projects, and analyses data with methods from statistical inference and learning.

Learning methods and activities

Lectures, alternatively guided self-study. Practical compulsory project in data analysis (application of course theory using R or Python) with oral presentation (pass/fail).

Compulsory assignments

  • Work

Specific conditions

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

TMA4267 Linear Statistical Methods, TMA4295 Statistical inference, TMA4300 Computer intensive statistical methods, TMA4268 Statistical learning or equivalent knowledge. Good understanding and experience with R, or with Python, for statistical data analysis.

Course materials

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. (The last corrected print is available from https://web.stanford.edu/~hastie/ElemStatLearn/.) In addition selected other material (chapters from books or journal articles) will be used. More detailed information will be given in the start of the course.

More on the course
Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2021

No.of lecture hours: 4
No.of specialization hours: 8

Language of instruction: -

Location: Trondheim

Subject area(s)
  • Statistics
Contact information
Course coordinator:

Department with academic responsibility
Department of Mathematical Sciences

Phone:

Examination

Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD work 30/100 ALLE
Room Building Number of candidates
Spring ORD work 30/100 ALLE
Room Building Number of candidates
Autumn ORD Oral examination 70/100 D
Room Building Number of candidates
Spring ORD Oral examination 70/100 D
Room Building Number of candidates
  • * 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.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU