1. Introduction to Artificial Intelligence (AI): definition of concepts, historical overview, successful applications in geoinformatics.
2. Exploratory data analysis: review of statistical foundations and their importance for machine learning.
3. Fundamentals of Machine Learning (ML): classification and regression, cluster analysis, supervised and unsupervised learning, illustrative examples.
4. Decision trees in AI and ML: principles of decision tree learning and their use in predictive analysis.
5. Logic and machine learning: specialization, generalization, logical consequence in AI.
6. Validation of machine learning results: training and test sets, overfitting, cross-validation, confusion matrix, learning curve.
7. Linear regression in ML: least squares method and its use in classification and prediction.
8. Kernel methods in ML: principles of kernel transformation and their applications in machine learning.
9. Neural networks in AI: multi-layer neural networks, backpropagation, and deep learning.
10. Cluster analysis in ML: k-nearest neighbors algorithm, hierarchical clustering, and their applications in geoinformatics.
11. Practical machine learning: data preprocessing, feature selection, feature engineering, sampling methods.
12. Verification and validation of ML models: evaluation of accuracy and reliability of predictive models.
13. Modern artificial intelligence and its applications: language models such as ChatGPT and their uses, generative AI, task automation in geoinformatics and other disciplines, ethical aspects of AI.
2. Exploratory data analysis: review of statistical foundations and their importance for machine learning.
3. Fundamentals of Machine Learning (ML): classification and regression, cluster analysis, supervised and unsupervised learning, illustrative examples.
4. Decision trees in AI and ML: principles of decision tree learning and their use in predictive analysis.
5. Logic and machine learning: specialization, generalization, logical consequence in AI.
6. Validation of machine learning results: training and test sets, overfitting, cross-validation, confusion matrix, learning curve.
7. Linear regression in ML: least squares method and its use in classification and prediction.
8. Kernel methods in ML: principles of kernel transformation and their applications in machine learning.
9. Neural networks in AI: multi-layer neural networks, backpropagation, and deep learning.
10. Cluster analysis in ML: k-nearest neighbors algorithm, hierarchical clustering, and their applications in geoinformatics.
11. Practical machine learning: data preprocessing, feature selection, feature engineering, sampling methods.
12. Verification and validation of ML models: evaluation of accuracy and reliability of predictive models.
13. Modern artificial intelligence and its applications: language models such as ChatGPT and their uses, generative AI, task automation in geoinformatics and other disciplines, ethical aspects of AI.