Akademik Dersler - İçerik

Analyzes the fundamental concepts, methods, and research areas of data and text mining.
Explains the fundamental concepts of data and text mining.
Evaluates application areas of data and text mining.
Analyzes data and text mining processes.
Applies appropriate data preprocessing and feature extraction techniques for large-scale data and text collections.
Applies data preprocessing and feature extraction techniques.
Utilizes text preprocessing methods for text analytics tasks.
ompares and evaluates classification, clustering, and association rule mining methods used in data and text mining.
Compares the performance of different classification algorithms.
Evaluates the strengths and limitations of clustering techniques.
Explains the generation and interpretation of association rules.
Develops appropriate methods for information retrieval, document analysis, and text mining problems.
Explains the fundamental components of information retrieval systems.
Applies document representation and indexing techniques.
Develops solutions for text classification and document clustering problems.
Evaluates the use of deep learning, transformer architectures, and large language models in data and text mining.
Compares deep learning-based approaches for text processing tasks.
Analyzes the working principles of transformer-based models.
Evaluates the advantages and limitations of large language models.