P000150, Introduction to Machine Learning, 4.0 Hp
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Syllabus

Level

Third cycle

Subject

Mathematical Statistics

Grading Scale

The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.

Course language

English

Entry Requirements

Objectives

The objective of the course is to provide an overview of machine learning methods. Upon completion, students will be able to:

• Identify and select appropriate predictive and classification models for various problems
• Implement machine learning models in R
• Apply cross-validation and resampling techniques for model assessment
• Interpret model results and evaluate their performance critically
• Communicate statistical findings effectively in written and oral form

Content

The course will cover the following topics:

• Principles of machine learning: over- and underfitting, bias-variance tradeoff, cross-validation
• Tree-based methods: Decision trees and ensemble methods
• Artificial neural networks (ANN)
• Unsupervised learning: PCA and clustering

Examination Formats and Requirements for Passing the Course

Passed exercises and passed examination in written and/or oral form.

Responsible Department/Equivalent

Department of Energy and Technology

Supplementary information

Other Information

Literature:

Joint course literature is established separately and is listed in a supplement to the course syllabus. Current information about joint course literature shall be made available not later than eight (8) weeks prior to course start.