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Lund University

Statistics: Deep Learning and Artificial Intelligence Methods

<p>Curious about how AI learns? This course gives you a practical and accessible introduction to neural networks and reinforcement learning. You’ll explore modern deep learning methods and see how they’re used in real-world situations. We’ll cover popular algorithms and models.</p><p>In this course you'll learn</p><…

  • Higher education
  • Information unavailable
  • 18 January 2027
  • Lund
  • Information unavailable
  • 50 %

Overview

<p>Curious about how AI learns? This course gives you a practical and accessible introduction to neural networks and reinforcement learning. You’ll explore modern deep learning methods and see how they’re used in real-world situations. We’ll cover popular algorithms and models.</p><p>In this course you'll learn</p><ul><li>The basics of machine learning and what you need to know to understand deep learning</li><li>Different types of neural networks: feed-forward, convolutional and recurrent</li><li>A brief history of AI and neural networks, plus current research questions in the field</li></ul><p>This course suits you if you study statistics, computer science, cognitive science or mathematics. It’s also relevant if you’re interested in language, logic, philosophy or psychology.</p>

Admission scores

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Entry requirements

STAN45 Statistics: Data Mining and Visualization, 7.5 ECTS, or STAN52 Statistics: Advanced Machine Learning, 7.5 ECTS, or DABN14 Data Analytics and Business Economics: Advanced Machine Learning, 7.5 ECTS, or 90 credits in Statistics and a course in linear algebra that covers matrix calculus, 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
2027-01-18
Measure
Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
Population
Education offering e.uoh.lu.stan47.62351.20271
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

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Study structure

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Application and important dates

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  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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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.stan47.62351.20271
Offering identity in the source
e.uoh.lu.stan47.62351.20271
Education-form source code
HS
Education code in the source
STAN47
Change time according to the source
2026-08-28T12:46:03

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.