Statistical Learning (STA530)

Introduction to statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis. 


Course description for study year 2022-2023. Please note that changes may occur.

See course description and exam/assesment information for this semester (2024-2025)
Facts

Course code

STA530

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Offered by
Time table

View course schedule

Content

Statistical learning, multiple linear regression, classification, resampling methods, model selection, regularization, non-linearity, tree-based methods, cluster analysis. 

Learning outcome

1. Knowledge. The student has knowledge about the most popular statistical models and methods that are used for prediction and inference in science and technology, with emphasis on regression and classification type statistical models.

2. Skills. The student knows, based on an existing data set, how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software (R). The student knows how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.

Required prerequisite knowledge

None

Recommended prerequisites

A basic course in probability and statistics equivalent to STA100 Probability and statistics 1. Basic university level mathematical analysis and linear algebra corresponding to MAT100 and MAT200. Experience with use of software, preferably R. At least one higher level course in statistics like for instance STA500 or STA510 is preferable but not an absolute requirement for taking the course. 

Exam

Coursework requirements

Compulsory assignments
2 compulsory exercises have to be approved in order to take the exam.

Course teacher(s)

Course coordinator:

Jan Terje Kvaløy

Course teacher:

Kaouther Hadji

Course coordinator:

Bjørn Henrik Auestad

Method of work

Lectures, exercises/datalab.

Open for

Admission to Single Courses at the Faculty of Science and Technology
Applied Data Science, Master of Science Degree Programme Computational Engineering - Master of Science Degree Programme Computer Engineering - Master of Science Degree Programme, Five Years Computer Science - Master of Science Degree Programme Computer Science - Master of Science Degree Programme, Part-Time Environmental Engineering - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme, Five Year Structural and Mechanical Engineering - Master of Science Degree Programme Structural and Mechanical Engineering - Master of Science Degree Programme. Five Years Mathematics and Physics - Master of Science Degree Programme Mathematics and Physics - Five Year Integrated Master's Degree Programme Offshore Field Development Technology - Master of Science Degree Programme Industrial Asset Management - Master's Degree Programme Marine and Subsea Technology, Master of Science Degree Programme, Five Years Marine and Offshore Technology - Master of Science Degree Programme Petroleum Geosciences Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme, Five Years Risk Management - Master's Degree Programme (Master i teknologi/siviling.)

Course assessment

Form and/or discussion.

Literature

The syllabus can be found in Leganto