Graduate study programme

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Pattern Recognition and Machine Learning DRbd1-06-18

ECTS 5 | P 30 | A 0 | L 30 | K 0 | ISVU 149804 | Academic year: 2019./2020.

Course groups

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Course lecturers

GRBIĆ RATKO, Lecturer
SLIŠKOVIĆ DRAŽEN, Lecturer
NOVOSELNIK FILIP, Associate

Goals

Introduce students to the principles and methods in the field of pattern recognition and machine learning. Present software tools for empirical data analysis and machine learning that enable pattern recognition problem solving and data mining in different areas of engineering as well as human activities in general. Introduce theoretical backgrounds for several courses that follow and are related to the application of pattern recognition theory.

Conditions for enrollment

Requirements met for enrolling in the study programme

Course description

Introduction to machine learning. Unsupervised and supervised learning. Parametric and nonparametric methods. Regression and classification methods. Neural networks. Support vector machines. Kernel methods. Data clustering. Dimensionality reduction and feature extraction. Model selection. Results validation. Basics of the decision theory. Different applications of machine learning and examples.

Student requirements

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Monitoring of students

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Obligatory literature

1. 1 Alpaydin, E. Introduction to Machine Learning MIT Press, 2014.

2. 2 T. Hastie, R. Tibshirani, J. Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer, 2009.


Pretraži literaturu na:

Recommended additional literature

1. 1 Haykin, S. Neural Networks – A Comprehensive Foundation, 2nd edition Prentice Hall, 1999.

2. 2 C.M. Bishop Pattern Recognition and Machine Learning Springer, 2007.

Course assessment

Conducting university questionnaires on teachers (student-teacher relationship, transparency of assessment criteria, motivation for teaching, teaching clarity, etc.). Conducting Faculty surveys on courses (upon passing the exam, student self-assessment of the adopted learning outcomes and student workload in relation to the number of ECTS credits allocated to activities and courses as a whole).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. define the basic concepts of pattern recognition theory and machine learning

2. suggest a way to solve a specific problem with a machine learning approach

3. develop your own software solution using appropriate libraries that contain implemented methods and machine learning algorithms

4. assess the suitability of a particular unsupervised learning algorithm for a given problem

5. assess the suitability of a particular supervised learning algorithm for a given problem

6. explain ways of model selection and evaluation



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