Courses with lecturer

Term

Language

Place

Form

Price without VAT

8. 4. 2025 09:00 - 17:00

Language

Place

online

Form

virtual classroom

Code of the course: PU21110287-0020

Price without VAT

4 990 Kč

Order

8. 4. 2025 09:00 - 17:00

Language

Place

Praha

Form

classroom

Code of the course: PU21110287-0021

Price without VAT

4 990 Kč

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Open term

Language

Place

Praha

Form

classroom

Code of the course: PU21110287-0003

Price without VAT

4 990 Kč

Order

Open term

Language

Place

Praha

Form

classroom

Code of the course: PU21110287-0004

Price without VAT

4 990 Kč

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Course description

We begin with examples of classical methods for modeling and prediction of time series and we continue to more advanced methods based on machine learning. We finish with a complex example of training time series model on historical data using neural network and we evaluate its performance in predicting future.

Required knowledge

basic knowledge of programing in Python
high school level of mathematics
Basics of machine learning on the level of our course Introduction to machine Learning

Course content

Introduction to the theory of time series modeling
Classical methods for time series prediction (space & frequency domain, spectral analysis, autocorrelation, ARIMA models etc.)
Hands-on example (pandas, basic characteristics, simple prediction)
Machine learning for time series prediction (state-space methods, Hidden Markov Chain, Kalman filter, classical neural networks, recurrent networks, LSTM)
Hands-on examples of machine learning methods (training set preparation for specific task and model, training process & evaluation)
Complex example of time series prediction using recurrent neural network (temperature prediction from high-dimensional input data: training data set preparation, training process & validation, prediction with trained neural network)

Lecturers

Dušan Fedorčák

He has been working in the field of machine learning for more than 10 years. During his time in academia, he dealt with self-organization, machine learning without a teacher, time series prediction and traffic modeling. Since 2014, he has been on the startup scene (GoodAI - research of general artificial intelligence, Neuron Soundware - sound processing, CEAI - fintech & NLP).

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