Subjects

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Fundamentals of AI in Chemical and Forensic Analysis - B402022

Title:	Základy AI v chemické a forenzní analýze
Guaranteed by:	Department of Analytical Chemistry (402)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2026
Semester:	both
Points:	3
E-Credits:	3
Examination process:
Hours per week, examination:	1/1, Ex [HT]
Capacity:	winter:unknown / unknown (unknown) summer:unknown / unknown (unknown)
Min. number of students:	unlimited
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Level:
Note:	course can be enrolled in outside the study plan enabled for web enrollment you can enroll for the course in winter and in summer semester

Guarantor:	Uhlíková Tereza doc. Mgr. Ph.D.
Classification:	Informatics > Database Systems

Examination dates Schedule

Annotation -

The course introduces students to the principles of Artificial Intelligence (AI) and its applications in chemical and forensic analysis. Emphasis is placed on practical understanding of AI fundamentals, data structures, algorithms, and the processing of experimental data. Students will become familiar with visual tools for machine learning (e.g., Orange), basic algorithmic thinking, and will gain an overview of current and emerging trends—including an example of quantum computing. No prior knowledge of computer science is required.

Last update: Uhlíková Tereza (21.05.2025)

Course completion requirements -

Active seminar participation – 50%

Oral final exam – 50%

Last update: Uhlíková Tereza (21.05.2025)

Literature -

Obligatory:

p. . : , , s. ISBN .

Last update: Uhlíková Tereza (21.03.2025)

Teaching methods -

Interactive lectures (using visualizations and software)

Practical exercises and project work

Group discussions, feedback, and self-assessment

Searching and interpreting scientific sources

Last update: Uhlíková Tereza (21.05.2025)

Requirements to the exam -

Last update: Uhlíková Tereza (21.03.2025)

Syllabus -

1: Introduction to AI and Its Applications in Chemistry

2: Data in Chemistry and Forensic Science

3: Basics of Computer Science for Chemists

4: Algorithm Complexity, Data Structures, and Formats

5: Fundamentals of Machine Learning (ML)

6: Neural Networks (Almost) Without Mathematics

7: Open-Source Tools for Working with AI

8: Lab I: Data Acquisition, Cleaning, and Preparation

9: Lab II: Classification of Chemical Samples

10: Lab III: Prediction of Substance Properties

11: Limits of AI, Overfitting, and Model Interpretation

12: Quantum Computing and Calculations in Chemistry

13: Project Workshop

14: Course Summary and Student Project Presentations

Last update: Uhlíková Tereza (21.05.2025)

Learning outcomes -

After completing the course, students will be able to:

Describe the basic concepts of AI, machine learning, and data analysis.

Explain the principles of algorithm complexity and data structures.

Use tools for visual data analysis (e.g., Orange).

Perform basic classification and prediction of chemical data.

Critically interpret the results of AI models.

Understand the ethical and technical limitations of AI.

Last update: Uhlíková Tereza (21.05.2025)

Entry requirements -

Knowledge within the scope of the course

Last update: Uhlíková Tereza (21.03.2025)

Registration requirements - Czech

žádné

Last update: Kaňa Antonín (24.05.2025)

Teaching methods
	Activity	Credits	Hours
	Účast na přednáškách	0.4	12
	Práce na individuálním projektu	1.1	30
	Příprava na zkoušku a její absolvování	1.1	30
	Účast na seminářích	0.4	12
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