Course detail
Data Analysis and Artificial Intelligence Methods
FSI-TPX Acad. year: 2026/2027 Winter semester
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.
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
Czech
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
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme B-FIN-P: Physical Engineering and Nanotechnology, Bachelor's
branch ---: no specialisation, 2 credits, compulsory
Type of course unit
Lecture
13 hours, optionally
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
Syllabus
Students solve problems and excercises defined in the lectures.