Professional study programme

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Applied Machine Learning SIR404-17

ECTS 5 | P 30 | A 0 | L 30 | K 0 | ISVU 175205 | Academic year: 2019./2020.

Course groups

Prikaži sve grupe na predmetu

Course lecturers

GRBIĆ RATKO, Lecturer
SLIŠKOVIĆ DRAŽEN, Lecturer

Goals

Familiarise students with the principles and methods in the field of machine learning and enable them to work with development tools and services that enable data analysis and machine learning.

Conditions for enrollment

The necessary requirements to enrol in the second year of the studies.

Course description

Introduction to machine mearning. Unsupervised and supervised learning. Parametric and nonparametric methods. Regression and classification methods. Model complexity. Model selection. Result evaluation. Different methods/algorithms of supervised machine learning: neural networks, support vector machines, decision trees, deep learning, etc. Data clustering algorithms. Data dimensionality reduction algorithms. An overview of current machine learning development environments. Model implementation. Different applications of machine learning (text processing, image processing, recommendation systems, etc.) 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 S. Raschka Python Machine Learning Packt Publishing, 2015.

2. 2 E. Alpaydin Introduction to Machine Learning MIT Press, 2014.


Pretraži literaturu na:

Recommended additional literature

1. 1 W. McKinney Python for Dana Analysis O Reilly, 2013.

2. 2 C. Rossant IPython Interactive Computing and Visualization Cookbook Packt Publishing, 2014.

3. 3 G. James, D. Witten, T. Hastie, R. Tibshirani An Introduction to Statistical Learning with Applications in R, 6th Ed. Springer, 2013.

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 basic terminology and the concept of machine learning

2. apply theoretical knowledge to solving a simple machine learning problem

3. use program implementations of machine learning methods and algorithms

4. apply exploratory data analysis techniques

5. apply data clustering algorithms

6. apply algorithms to solve classification and regression problems



Aktivnosti studenta: Vidi tablicu aktivnosti

Student's activity Workload ECTS (Workload/30) Learning outcomes
Upon successful completion of the course, students will be able to:
Teaching
method
Assessment method Points
Pohađanje Predavanja (PR), Auditorne vježbe (AV), Laboratorijske vježbe (LV)451.51,2,3,4,5,6Predavanja (PR), Laboratorijske vježbe (LV)Predavanja (PR), Laboratorijske vježbe (LV)710
Pisanje priprema za LV, analiza rezultata, te pisanje izvještaja451.52,3,4,5Laboratorijske vježbe (LV)Provjera pripreme za LV, nadzor provođenja LV-a, provjera napisanih izvještaja1530
Priprema za usmeni ispit i usmeno odgovaranje na pitanja451.51,2,4,6Usmeni ispitProvjera danih odgovora1835
Rješavanje projektnog zadatka150.52,3,4,5,6ProjektProvjera rješenja projektnog zadatka025