Navigation

13M111PSZ - Data Mining and Semantic Web

Course specification
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.edu.rs/rti/ms1psz/
    URL to lectures https://teams.microsoft.com/l/team/19%3Ass0hHEeG2hAJ2F9A8jxgM9rMr9SA0C8isUj6UD58hNg1%40thread.tacv2/conversations?groupId=4e34514a-ef67-4150-8d00-cb521856bfce&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
    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
    1. Andreas Müller, Sarah Guido: "Introduction to Machine Learning with Python: A Guide for Data Scientists", O'Reilly Media; 1 edition (October 21, 2016)
    2. Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal: "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, 4th Edition (2016)
    3. C.Albon: "Machine Learning with Python Cookbook", O'Reilly Media; 1 edition (March 9, 2018)
    4. J.VanderPlas: "Python Data Science Handbook: Essential Tools for Working with Data", O'Reilly Media; 1 edition (November 21, 2016)
    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