13M111PSZ - Data Mining and Semantic Web
Course specification | ||||
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Course title | Data Mining and Semantic Web | |||
Acronym | 13M111PSZ | |||
Study programme | Electrical Engineering and Computing | |||
Module | ||||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | none | |||
The goal | The objective of the course is to introduce students to the most popular models of machine learning and the methodology of their proper use and evaluation. | |||
The outcome | After the completion of the course, the student is expected to: 1) demonstrate understanding of the problem, apply algorithms and ML techniques and define their own problem solving models; 2) acquire a sense of exploration, processing and analysis of data and presentation of the results; 3) learn to develop own application or use existing software tools and libraries. | |||
Contents | ||||
URL to the subject page | http://rti.etf.bg.ac.rs/rti/ms1psz/ | |||
Contents of lectures | Introduction to machine learning. Training and evaluation of models in supervised machine learning. Naive Bayesian Classifier. Linear regression. Logistic regression. Support vector method. K nearest neighbors. Decision trees. Advanced Machine Learning Techniques - Reinforcement Learning, Deep Learning, and others. | |||
Contents of exercises | Visual simulations of theoretical problems. Solving and demonstrations of practical tasks. Analysis of the latest research and scientific papers in this field. | |||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
2 | 2 | |||
Methods of teaching | Lectures with presentations, interactive practical exercises, individual work on projects, laboratory exercises with visual simulations | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | 0 | Test paper | 40 | |
Practical lessons | 40 | Oral examination | 0 | |
Projects | 20 | |||
Colloquia | 0 | |||
Seminars | 0 |