This offering is not in the current catalogue. The information is retained from an earlier publication. Check the provider's current offering.
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
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
Study structure
Application and important dates
- Programme or course starts
- Programme or course ends
Salary and salary distribution
Common occupations after graduation
Students
Geographical background
Previous upper-secondary schools and programmes
Completion and outcomes
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.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.