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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 2021-2022

Facts
Course code

STA530

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Learning outcome

1. Knowledge. The student has knowledge about the most popular statistical models and methods that are used for prediction 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.

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

Projectworks and written exam

Form of assessment Weight Duration Marks Aid
Projectworks 1/2 A - F
Written exam 1/2 4 Hours A - F

The course consists of two 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 does 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.

Course teacher(s)
Course coordinator: Jan Terje Kvaløy
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's degree Programme Computational Engineering, Master's Degree Programme Data Science - Master's Degree Programme - 5 year Computer Engineering - Master's Degree Programme - 5 year Computer Science - Master's Degree Programme Computer Science - Master's Degree Programme - part time Environmental Engineering - Master of Science Degree Programme Industrial economics - Master's Degree Programme Industrial economics - Master's Degree Programme - 5 year Engineering Structures and Materials - Master's Degree Programme Mechanical and Structural Engineering and Materials Science- Master's Degree Programme - 5 years Mathematics and Physics - Master of Science Degree Programme Mathematics and Physics, 5-year integrated Master's Programme Offshore Field Development Technology - Master's Degree Programme Industrial Asset Management - Master's Degree Programme Marine- and Subsea Technology - Five-Year Master's Programme Marine- and Offshore Technology - Master's Degree Programme Petroleum Geosciences Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme Petroleum Engineering - Master`s Degree programme in Petroleum Engineering, 5 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