Statistical Learning (STA530)

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


Course description for study year 2023-2024. Please note that changes may occur.

Facts

Course code

STA530

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Content

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

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

Portfolio and written exam

Form of assessment Weight Duration Marks Aid
Portfolio with two hand inns 1/2 Letter grades
Written exam 1/2 4 Hours Letter grades

Project work and written exam, assessed with letter grades.The course has two assessment parts. 1) Project work that will count 50 % of the overall grade, 2) A written final exam that will count 50 % of the overall grade. Both the project work and the exam must be passed in order to obtain an overall grade in the course. Candidates that do not pass the project work, cannot resubmit until the next time the course is lectured.The project work consists of two parts that are equally weighted. The final grade of the project work is given when all parts have been submitted and the project work as a whole is graded.There is no resit exam in the portofolio

Coursework requirements

Compulsory assignments

Course teacher(s)

Course coordinator:

Jan Terje Kvaløy

Course teacher:

Kaouther Hadji

Course coordinator:

Bjørn Henrik Auestad

Method of work

Lectures, exercises/datalab, project work.

Open for

Admission to Single Courses at the Faculty of Science and Technology Data Science - Master of Science Degree Programme Computational Engineering - Master of Science Degree Programme Data Science - Master of Science Degree Programme, Five Years 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 of Science 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

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto