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Lund University

Statistics: Advanced Machine Learning

<p>What if you could train a computer to learn from data - and improve over time, just like humans do? Machine learning is a powerful set of tools for uncovering patterns, making predictions, and supporting data-driven decisions.</p><p>Building on the foundations from STAN51 Statistics: Machine Learning from a Regre…

  • Higher education
  • Information unavailable
  • 2 November 2026
  • Information unavailable
  • Information unavailable
  • 50 %

Overview

<p>What if you could train a computer to learn from data - and improve over time, just like humans do? Machine learning is a powerful set of tools for uncovering patterns, making predictions, and supporting data-driven decisions.</p><p>Building on the foundations from STAN51 Statistics: Machine Learning from a Regression Perspective, this course introduces you to more advanced machine learning techniques with a focus on applications in business and economics.You will learn about:</p><ul><li>bootstrap methods for assessing model stability and uncertainty</li><li>ensemble techniques such as boosting and random forests for improving predictive performance</li><li>unsupervised learning methods including principal component analysis and clustering</li><li>applied machine learning for real-world problems, including causal inference in economic and business contexts.</li></ul>

Admission scores

Uppgift saknasVerified data is not connected to this education offering.

Entry requirements

90 credits in Statistics with at least 7.5 credits in Regression Analysis or Econometrics and also STAN51 Statistics: Machine Learning from a Regression Perspective, or the equivalent.

The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.

Source, measure and data quality
Source
Skolverket Susa-navet
Period
2026-11-02
Measure
Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
Population
Education offering e.uoh.lu.stan52.e1065.20262
Last checked
2026-09-23T10:38:33.477975+00:00
Limitation
Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.

Programme content

Uppgift saknasVerified data is not connected to this education offering.

Study structure

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Application and important dates

  1. Programme or course starts
  2. Programme or course ends

Salary and salary distribution

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Common occupations after graduation

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Students

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Geographical background

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Previous upper-secondary schools and programmes

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Completion and outcomes

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About the provider

Lund University

Provider for the published education offering.

Sources and data quality

Education facts for the selected offering come from Skolverket Susa-navet.

Retrieved . Published . Times are shown in Swedish local time.

Source identity and publication version
Publication version
8e217193-f5fa-4778-b085-a4521fd03e8d
Education identity in the source
i.uoh.lu.stan52.e1065.20262
Offering identity in the source
e.uoh.lu.stan52.e1065.20262
Education-form source code
HS
Education code in the source
STAN52
Change time according to the source
2026-02-10T09:40:13

The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.