Course detail

Data Analysis and Artificial Intelligence Methods

FSI-TPXAcad. year: 2026/2027

The course develops students' ability to process and analyze physical data using statistical and artificial intelligence methods. Students will learn the principles of parameter estimation, hypothesis testing, classification, regression, and modern machine and reinforcement learning methods.

Language of instruction

Czech

Number of ECTS credits

Assignment to study programme types

Bachelor's

Mode of study

Not applicable.

Guarantor

doc. Ing. Miroslav Bartošík, Ph.D.

Department

Institute of Physical Engineering (ÚFI)

Entry knowledge

Mathematics, namely calculation of the basic statistical characteristics of the data set.

Rules for evaluation and completion of the course

The assessment of a student is made upon his/her performance in practicals and the competence in a discussion during the colloquium (of the topics related to the solution of the final problem of the course).
Attendance at practicals is obligatory and is monitored by the tutor. Absence may be compensated for by the agreement with the tutor depending on the length of absence.

Aims

The student:
• analyzes physical data using statistical and AI methods,
• can apply MLE and hypothesis testing to experimental data,
• uses regression and classification methods (Naive Bayes, SVM, neural networks),
• performs dimension reduction (PCA) and clustering,
• understands the principles of Bayesian and reinforcement learning,
• interprets model results in the context of physical processes

Study aids

https://sites.google.com/view/pave1

Prerequisites and corequisites

Not applicable.

Basic literature

Barber, D. Bayesian Reasoning and Machine Learning. Cambridge: Cambridge University Press, 2012. ISBN 978-0521518147.
Géron, A. Hands-On Machine Learning with Scikit-Learn and TensorFlow. Sebastopol: O’Reilly Media, 2017. ISBN 978-1491962299. (EN)
VanderPlas, J. Python Data Science Handbook. Sebastopol: O’Reilly Media, 2016. ISBN 978-1491912058. (EN)

Type of course unit

Lecture

13 hours, optionally

Teacher / Lecturer

doc. Ing. Miroslav Bartošík, Ph.D.

Syllabus

Syllabus
Week 1: Introduction to Data Analysis and Workflow – Overview of ML and AI in Physics, Pandas and Matplotlib Review, Data Types, Analysis Workflow, Probability
Week 2: Statistical Data Analysis – Mean, Variance, Distribution, Histograms, Uncertainty Estimation
Week 3: Parameter Estimation and Maximum Likelihood (MLE) – Likelihood, Log-Likelihood, Numerical Maximization, Applications to Physics Data
Week 4: Statistical Hypothesis Testing – Principles of Testing, P-Value, Significance, T-Test, χ²-Test, Measurement Agreement Test
Week 5: Regression Methods – Linear and Nonlinear Regression, Least Squares, Relationship to MLE, Quality of Fit
Week 6: Data Classification: Naive Bayes and Support Vector Machines (SVM) – Supervised Learning, Kernel Functions, Applications in Physics
Week 7: Clustering and PCA Methods – Unsupervised Learning, k-means, PCA – dimension reduction, visualization of physical data
Week 8: Neural networks – perceptron architecture, activation functions, training, TensorFlow/Keras
Week 9: Regression and classification using neural networks – prediction of physical quantities, regularization, overfitting
Week 10: Bayesian modeling – Bayesian inference, MAP estimation, relation to MLE, interpretation in physics
Week 11: Reinforcement Learning (basics) – agent, environment, reward, simple RL simulations (e.g. pendulum stabilization)
Week 12: Project: Analysis of physical data using ML/AI – individual or team work, presentation and interpretation of results

Exercise

13 hours, compulsory

Teacher / Lecturer

doc. Ing. Miroslav Bartošík, Ph.D.

Syllabus

Students solve problems and excercises defined in the lectures.