PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2025
Extent and Intensity
2/0/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
In-person direct teaching
Teacher(s)
doc. RNDr. Jan Sedmidubský, Ph.D. (lecturer)
RNDr. Ondřej Sotolář (assistant)
Guaranteed by
doc. RNDr. Jan Sedmidubský, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Prerequisites
A student should be familiar with the basics of machine learning (e.g., IB031 Introduction to Machine Learning).
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 37 fields of study the course is directly associated with, display
Course objectives
By the end of the course, students should know the core principles of popular machine-learning and data-mining methods and should know how such methods are applied in selected application use cases. In addition, the students should gain practical experience by implementing a selected data-mining method.
Learning outcomes
By the end of the course, students will
- understand principles of advanced data mining and machine learning methods;
- know how to apply specific algorithms to real-life data;
- be able to implement and validate a selected data analysis method.
Syllabus
  • Introduction to machine learning and data mining. Project proposals.
  • Metric learning, product quantization, approximate nearest-neighbor search.
  • Advanced clustering methods.
  • Advanced anomaly detection.
  • Bayesian optimization.
  • Automated machine learning.
  • Time-series data mining.
  • Cross-modal learning.
  • Applied deep learning: examples of real-life applications.
Literature
  • HAN, Jiawei, Pei JIAN and Hanghang TONG. Data mining: concepts and techniques. 4th Edition. 2022. ISBN 978-0-12-811760-6. info
Teaching methods
Lectures + project.
Assessment methods
Written exam + defense of a project.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
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