SylabUZ
Course name | Basics of Machine Learning |
Course ID | 11.3-WK-IiEP-PUM-S18 |
Faculty | Faculty of Exact and Natural Sciences |
Field of study | computer science and econometrics |
Education profile | academic |
Level of studies | First-cycle studies leading to Bachelor's degree |
Beginning semester | winter term 2019/2020 |
Semester | 3 |
ECTS credits to win | 5 |
Course type | optional |
Teaching language | polish |
Author of syllabus |
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The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
Lecture | 30 | 2 | - | - | Credit with grade |
Laboratory | 30 | 2 | - | - | Credit with grade |
The assumption of the course is to familiarize students with the basic machine learning algorithms that are currently very widely used in the practical analysis of various types of databases.
The aim of the course is to acquire by the student the ability to select appropriate machine learning methods depending on the practical business problem.
Analyzes of real data will be carried out using R software, which is currently very popular among analysts. After this course, the student will have the ability to use specialized R program libraries to solve specific problems using machine learning algorithms.
Basic knowledge of statistics and probability theory.
Lecture / Laboratory:
Lectures: traditional or online form.
Laboratory: solving research problems with the use of machine learning algorithms with the use of specialized libraries of the R program. Discussion on possible solutions to the given problems. Teamwork.
Outcome description | Outcome symbols | Methods of verification | The class form |
Checking the level of preparation of students and their activity both in the laboratory and during the lecture.
The laboratory grade will be issued on the basis of the test results and / or projects.
Lantz B., Machine learning with R. PACKT 2013.
T. Morzy, Eksploracja danych – metody i algorytmy, Wydawnictwo naukowe PWN, Warszawa, 2013.
M. Szeliga, Data science i uczenie maszynowe, Wydawnictwo naukowe PWN, Warszawa, 2017.
Christopher M. Bishop Pattern Recognition and Machine Learning, Springer, 2007
Modified by dr Alina Szelecka (last modification: 21-11-2020 06:10)