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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
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.
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About the provider
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.