From Natural Science to Data Science: An Introduction to Artificial Intelligence
0600-OG-EN-AIAI
The course is designed for general university students with a background in mathematics. It consists of 30 hours of lectures covering the basics of AI, its historical context, key algorithms (e.g., regression, classification, decision-trees, neural networks, and its role in modern science. The lectures show practical applications of AI tools such as Python, Scikit-Learn, PyTorch, TensorFlow, and different datasets. Ethical considerations and limitations of AI are also discussed.
Total student workload
Contact hours with teacher:
- participation in lectures: 30 hrs
- consultations: 10 hrs
Self-study hours:
- preparation for presentations: 10 hrs
- reading literature: 10 hrs
Altogether: 60 hrs
Learning outcomes - knowledge
• The graduate knows the fundamentals of artificial intelligence, including its definition, principles, and its applications in basic science.
• The graduate has structured knowledge of key algorithms and models used in machine learning, including: linear regression, least square regression, k-nearest neighbours, support vector machines, classification and regression trees and artificial neural networks. The graduate understands how these models apply to data analysis and property prediction.
• The graduate understands the ethical implications and challenges of AI in science, including potential biases, data security issues and societal and environmental impacts.
Learning outcomes - skills
• The graduate is able to apply AI techniques to solve basic problems.
• The graduate is able to evaluate the suitability of different ML approaches to answer scientific questions based on data.
• The graduate is able to use Python libraries and standard ML protocols to train and optimize predictive models.
• The graduate is able to evaluate the quality, accuracy and predictive power of ML models applied to scientific datasets.
Learning outcomes - social competencies
• The graduate recognizes the significance of AI in natural sciences, fostering innovation and discovery.
• The graduate demonstrates readiness to adopt interdisciplinary approaches that integrate AI and natural science to solve complex problems and explore novel solutions.
• The graduate is aware of ethical considerations and potential risks associated with the use of AI tools, ensuring their responsible application in scientific and industrial contexts.
• The graduate develops strong interpersonal skills such as communication, teamwork and problem-solving abilities, fostering collaboration in multidisciplinary projects.
Teaching methods
- informative lecture
- discussion
- presentation of a paper
Observation/demonstration teaching methods
- simulation (simulation games)
- staging
Expository teaching methods
- informative (conventional) lecture
- description
- problem-based lecture
- participatory lecture
Exploratory teaching methods
- practical
- points system
- seminar
- brainstorming
- presentation of a paper
Online teaching methods
- exchange and discussion methods
- evaluative methods
- methods developing reflexive thinking
- content-presentation-oriented methods
Prerequisites
General Mathematics
No prior knowledge of programming is required
Course coordinators
Assessment criteria
Assessment methods:
- seminar: 75 pts
- class activity: 25 pts
Assessment criteria:
fail- 0 - 49 pts (< 50 %)
satisfactory- 50 - 59 pts (< 60 %)
satisfactory plus- 60 - 65 pts (< 65 %)
good – 66 - 75 pts (< 75 %)
good plus- 76 - 80 pts (< 80 %)
very good- 81 - 100 pts (< 100 %)
Additional information
Additional information (registration calendar, class conductors,
localization and schedules of classes), might be available in the USOSweb system: