SylabUZ
Nazwa przedmiotu | Machine Learning |
Kod przedmiotu | 11.3-WK-CSEED-ML-S22 |
Wydział | Wydział Matematyki, Informatyki i Ekonometrii |
Kierunek | Computer science and econometrics |
Profil | ogólnoakademicki |
Rodzaj studiów | drugiego stopnia z tyt. magistra |
Semestr rozpoczęcia | semestr zimowy 2022/2023 |
Semestr | 4 |
Liczba punktów ECTS do zdobycia | 5 |
Typ przedmiotu | obieralny |
Język nauczania | angielski |
Sylabus opracował |
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Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 15 | 1 | - | - | Zaliczenie na ocenę |
Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
The aim of the course is to prepare students to solve practical problems using both classical statistical models
neural network algorithms and other known machine learning methods. An important goal of this subject is to develop reasoning skills,
analytical thinking and selection of appropriate machine learning algorithms for a given problem.
Analyzes of real data will be performed using one of the two programs most often used by analysts: R or Python. After this course
the student will have the ability to use specialized libraries of the selected program to solve specific problems using
machine learning algorithms.
Knowledge of the basics of statistics and probability theory. Statistical data analysis.
Lecture/Lab:
1. Introduction to machine learning. Machine learning methods as a decision support technique.
2. Classification of machine learning methods. Supervised and unsupervised learning methods.
3. Data preprocessing and scaling. Cross-validation.
4. Unsupervised learning. Cluster analysis algorithms: hierarchical clustering, K-means method. Dimensionality reduction.
5. Association rules.
6. Supervised learning techniques. Classification and regression algorithms: linear regression model, logistic model, decision trees, artificial neural networks.
7. Assessment of the quality of models. Learning curves.
Lecture: traditional and problem-based, multimedia presentation.
Laboratory: the laboratory program includes deepening the issues discussed during lectures. Solving research problems using machine learning algorithms
using specialized R or Python libraries. Teamwork. Discussion related to the use of appropriate algorithms and interpretation of intermediate and final results.
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Checking the degree of students' preparation and their activity both in the laboratory and during the lecture.
The skills acquired during laboratory classes are verified on the basis of the student's performance of specified tasks and problems settled in the form of reports.
The passing threshold is 50% of the total points.
Zmodyfikowane przez dr Magdalena Wojciech (ostatnia modyfikacja: 18-02-2024 19:02)