Data is displayed for academic year: 2023./2024.
Data mining - definitions and areas of application. Types of data. Data sources and their acquisition. Data preprocessing - data manipulation, data filtering, data transformation. Unbalanced datasets. Machine learning algorithms for data processing: feature selection methods, classification algorithms, clustering methods, association rules. Models with clear interpretation based on induction rules. Classifier ensembles. Model explainability. Time series analysis. Deep learning in data mining. Deep learning architectures in applications. Specificities of data mining in different fields of application. Use of freely available tools for data mining. Data mining project.
[FER3-EN] Data Science - profileRecommended elective courses (2. semester)
- identify any potential shortcomings of the analyzed data set
- evaluate the suitability of the used sequence of machine learning methods in various fields of application
- combine feature selection methods on a given problem
- analyze the given data set using a suitable sequence of machine learning methods in at least one existing software tool
- develop your own software to analyze a particular dataset
- classify machine learning techniques by the type of problem they are solving
- analyze time series from different domains with predictive analytics techniques
- construct explainable machine learning models to facilitate reaching decisions in specific domain
Forms of Teaching
Lectures - theoryIndependent assignments
Data mining project
|Type||Threshold||Percent of Grade||Threshold||Percent of Grade|
|Seminar/Project||40 %||60 %||40 %||60 %|
|Final Exam: Written||40 %||40 %|
|Exam: Written||40 %||40 %|
Week by Week Schedule
- Course administration. Introduction to data mining. Description of the field. Data mining process models. References.
- Data preparation for data mining: data preparation process, problems in data and their solutions. Examples. Project.
- Data transformation, dimensionality reduction and feature extraction. Project.
- Feature selection: filter methods, wrapper methods, embedded methods, hybrid methods. Examples. Project.
- Imbalanced data, concept drift. Algorithms for solving these problems. Project.
- Classification and regression ensembles. Ensemble algorithms. Ensemble models' explanation methods. Project.
- Interpretable machine learning. Rules induction. Algorithms for induction rules. Project.
- Frequent pattern mining and association rules. High-utility itemset mining. Applications in recommender systems. Algorithms. Project.
- Time series data mining: introduction and terminology. Time series analysis components. Feature extraction-based time series analysis. Project.
- Time series data mining: classification and prediction algorithms. Project.
- Deep learning in data mining: introductory topics. Project.
- Deep learning in data mining: architectures in application areas: natural language processing, time series classification, image classification, image generation from text. Project delivery.
- Project presentations
- Final exam
(.), Witten IH, Frank E, Hall MA, Pal CJ. Data Mining: Practical Machine Learning Tools and Techniques. 4th ed. Morgan Kaufmann, 2016.,
(.), Fuernkranz J, Gamberger D, Lavrač N. Foundations of Rule Learning. Heidelberg : Springer, 2012,
(.), James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning: with Applications in R. Springer, 2014.,
(.), Raschka S, Mirjalili V. Python Machine Learning. 2nd ed. Packt Publishing, Birmingham UK, 2017.,
(.), Ryza S, Laserson U, Owen S, Wills J. Advanced Analytics with Spark: Patterns for Learning from Data at Scale. 2nd ed. O'Reilly Media, Sebastopol CA, USA, 2017.,
(.), Mitchell, R. Web Scraping with Python: Collecting more data from the Modern Web. 2nd ed. O'Reilly Media, Sebastopol CA, USA, 2018.,
L1 English Level
18 Laboratory exercises
0 Project laboratory
75 Very Good