Data Mining
Code  Completion  Credits  Range  Language 

BIEVZD  Z,ZK  4  2P+2C 
 Lecturer:
 Karel Klouda
 Tutor:
 Karel Klouda
 Supervisor:
 Department of Applied Mathematics
 Synopsis:

Students are introduced to the basic methods of discovering knowledge in data. In particular, they learn the basic techniques of data preprocessing, multidimensional data visualization, statistical techniques of data transformation, and fundamental principles of knowledge discovery methods. Students will be aware of the relationships between model bias and variance, and know the fundamentals of assessing model quality. Data mining software is extensively used in the module. Students will be able to apply basic data mining tools to common problems (classification, regression, clustering).
 Requirements:

The knowledge of calculus, linear algebra and probability theory is assumed.
 Syllabus of lectures:

1. Introduction to the field and applications
2. Decision trees, test, train, validation set
3. Ensemble methods (random forest, AdaBoost)
4. Hierarchical clustering, kmeans algorithm
5. kNN (knearest neighbours)
6. Naive Bayes
7. Linear regression
8. Logistic regression
9. Ridge regression and regularisation
10. Dimensionality reduction
11. Neural networks
12. Natural language processing
 Syllabus of tutorials:

1. Jupyter notebooks and machine learning packages
2. Decision trees, hyperparameters tuning
3. Ensemble methods (random forest, AdaBoost)
4. Hierarchical clustering, kmeans algorithm
5. kNN (knearest neighbours), crossvalidation
6. Naive Bayes classifier
7. Linear regression
8. Logistic regression
9. Ridge regression
10. Dimensionality reduction
11. Neural networks
12. Natural language processing
 Study Objective:

The module aims to introduce students to a rapidly developing field  knowledge discovery in data.
 Study materials:

1. Data Mining: Practical Machine Learning Tools and Techniques, I. H. Witten, E. Frank, M. A. Hall, Elsevier, 2011, ISBN 9780080890364.
2. Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, MIT Press, 2016, ISBN 9780262035613.
3. Machine Learning: A Probabilistic Perspective, K. P. Murphy, MIT Press, 2012, ISBN 9780262018029.
 Note:
 Further information:
 https://courses.fit.cvut.cz/BIEVZD/
 Timetable for winter semester 2019/2020:
 Timetable is not available yet
 Timetable for summer semester 2019/2020:
 Timetable is not available yet
 The course is a part of the following study plans:

 Bc Branch Security and Information Technology, Presented in English, Version 2015 to 2019 (elective course)
 Bc. Branch WSI, Specialization Software Engineering, Presented in English, Version 2015, 16, 17, 18 (elective course)
 Bc. Branch Computer Science, Presented in English, Version 2015 to 2019 (compulsory course of the specialization)