Elements of AI
Elements of AI
Description
The module aims to give an introduction and overview of artificial intelligence. The focus is on basic and important components of AI: problem solving, neural network, AI techniques such as search and machine learning as well as consequences of AI on society.
The Team
Teamchers
These are the teamchers you'll work with on the challenge.
Learners
- Study period
- 2 February – 5 June 2026
- Study format
- Online
- Application period
- 19 January – 24 May 2026
- Credits
- 2 ECTS
- Hosting university
- Linkoping University
- Topics
Information
Value and progress
Bayes rule
Apply the Bayes rule to infer risks in simple scenarios.
ESCO SKILLS
Machine learning techniques
Explain why machine learning techniques are used.
ESCO SKILLS
Classification methods
Explain the principles of some supervised classification methods.
ESCO SKILLS
Bayesian reasoning
Explain the base-rate fallacy and how to avoid it by applying Bayesian reasoning.
ESCO SKILLS
Neutral networks
Explain what a neural network is and where they are being successfully used.
ESCO SKILLS
Machine learning scenarios
Distinguish between unsupervised and supervised machine learning scenarios.
ESCO SKILLS
Formulate a research problem
Formulate a simple real-world problem as a search problem.
ESCO SKILLS
Ability to evaluate
Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI.
ESCO SKILLS
Basic programming
Understand the technical methods that underpin neural networks.
ESCO SKILLS
Critical thinking
Express some basic philosophical problems related to AI.
ESCO SKILLS
Basic knowledge about AI
Distinguish between realistic and unrealistic AI (science fiction vs. real life).
ESCO SKILLS
Societal implications
Identify some of the major societal implications of AI.
ESCO SKILLS
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Hosting university
Linkoping University
