MLA310 Matrix Methods for Data Analysis and Machine Learning
Credits (ECTS):10
Course responsible:Ulf Geir Indahl
Campus / Online:Taught campus Ås
Teaching language:Engelsk, norsk
Course frequency:The subject is not taught in autumn 2024 (from spring 2025, the course is taught every spring semester).
Nominal workload:250 hours
Teaching and exam period:The course is implemented and censored each spring semester. The subject is not taught in 2024.
About this course
Derivation and applications of advanced matrix methods for pattern recoginition, machine learning and data analysis: The subjects include clustering, projection- and matrix factorization methods, variable selection and regularization for regression- and classification problems. We will also cover efficient computations for model selection and -validation.
Learning outcome
The students will learn both the theoretical background and how to implement various methods for advanced analysis of research data.
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Teaching hours