Admission| aktivní účast | |
| Osvědčení o absolvování programu | |
| nejsou uvedeny | |
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LLL programme details
Advanced Data Analysis Methods and Neural Networks: Applications in Practice (C-K446-0002)
| Advanced Data Analysis Methods and Neural Networks: Applications in Practice | |
| full-time | |
| Czech | |
| "The course offers participants a comprehensive insight into advanced data analysis methods and neural networks and their practical utilization. It includes sophisticated data analysis techniques, such as cluster analysis and pattern recognition using neural networks. Participants will learn to apply principal component analysis, which enables the identification of key characteristics of data sets and the reduction of their dimensionality. They will also become familiar with algorithms such as support vector machine (SVM) and k-nearest neighbors, which are suitable for solving classification problems. In the course, participants will also be introduced to deep neural networks with an emphasis on time series prediction and analysis of multidimensional data. They will learn about a variety of currently used architectures of deep neural networks and how to apply them to analyze their own datasets. An important part of the course is validation and evaluation of classification and prediction accuracy using neural networks. Participants will learn how to properly set up and evaluate performance metrics of their models and how to effectively utilize various validation and optimization techniques. Overall, the course enables participants to gain a deeper understanding of advanced data analysis methods and neural networks and acquire practical skills for their application in various domains, from industrial applications to scientific research. This equips participants with the necessary knowledge and skills for successful work in the field of data analysis and artificial intelligence." |
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| Advanced Data Analysis Methods and Neural Networks: Applications in Practice | |
| Introduction to Cluster Analysis and Selected Cluster Analysis Methods 1: k-nearest neighbors, k-means, DBSCAN Application of selected classification methods using the Scikit-learn library and their comparison Hierarchical Clustering (Dendrogram) and Selected Cluster Analysis Methods 2: Self-Organizing Maps Application of selected classification methods using the sklearn-som or minisom library and their comparison Introduction to Neural Networks, Network Learning, Gradient Methods, Multi-Layer Perceptron (MLP) MLP - Universal Approximator, Neural Network Learning, Classification and Regression Application of Neural Networks for Time Series Processing Time Series Prediction Application of Neural Networks for Image Data Processing Issues in classification, object detection, tracking Use Case - Demonstration of Workflow in Processing a New Dataset Example of a Regression Dataset Use Case - Demonstration of Workflow in Processing a New Dataset Example of a Classification Dataset Creating Automatic Reports from Processed Data Automation of Report Generation with Results |
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| 50 | |
| basics of Python programming | |
| podání přihlášky a zaplacení účastnického poplatku |
Course requirements and qualification acquired
shrink expandGuarantor and venue
shrink expandVrba Jan Ing. Ph.D. Department of Mathematics, Informatics and Cybernetics Department of Mathematics, Informatics and Cybernetics Advanced Data Analysis Methods and Neural Networks: Applications in Practice Steinbach Jakub Ing.
Vrba Jan Ing. Ph.D.Dates and duration
shrink expand2024/2025 summer semester 17.03.25 1 32 hours 8 lekcí po 4 hodinách = 32 hodin Fees and financing
shrink expand14900 Kč / course Application
shrink expandOn-line Steinbach Jakub Ing. Jakub.Steinbach@vscht.cz 220443773 01.05.2024 - 30.03.2025