Advanced Machine Learning WM-I-WMUM

The purpose of the course is to provide knowledge and skills in advanced data analysis and the basics of machine learning, including neural networks. In this class, students will be introduced to advanced data analysis methods and machine learning models in R and Python. They will acquire the skills to: load a dataset, identify its flaws and improve them, select an appropriate model and evaluate it. They will also become familiar with advanced libraries and frameworks in R and Python for data analysis in science and business.

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

information and communication technology

(in Polish) E-Learning

Term 2021/22_Z:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

Term 2024/25_Z:

(in Polish) E-Learning

Term 2023/24_Z:

(in Polish) E-Learning

Term 2022/23_Z:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

Term 2020/21_Z:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

(in Polish) WYKŁAD Szacunkowy nakład pracy studenta: - uczestnictwo w zajęciach: 30h, - uczestnictwo w egzaminie: 3h, - konsultacje z prowadzącym: 3h, - przygotowanie do zajęć: 5h, - przygotowanie do egzaminu: 35h, razem 76h, co odpowiada 3 ECTS. LABORATORIA: Szacunkowy nakład pracy studenta: - uczestnictwo w zajęciach: 30h, - uczestnictwo w kolokwium: 2h, - konsultacje z prowadzącym: 3h, - przygotowanie do zajęć: 5h, - przygotowanie projektu: 35h, razem 76h, co odpowiada 3 ECTS.

Subject level

advanced

Learning outcome code/codes

LECTURE I2_W10, I2_U09 LABORATORIES I2_W10, I2_U09

Type of subject

optional with limited choices

Preliminary Requirements

Calculus I, algebra, probability and statistics, structured programming, modern data analysis technologies

Course coordinators

Term 2023/24_Z:

Dorota Dąbrowska
Robert Kłopotek

Term 2019/20_Z:

Term 2021/22_Z:

Term 2020/21_Z:

Term 2022/23_Z:

Term 2024/25_Z:

Assessment criteria

For all learning outcomes, the following assessment criteria are adopted for all forms of verification:
grade 5: fully achieved (no obvious shortcomings),
grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met,grade 4: largely achieved and the criteria for a higher grade are not met,
grade 3.5: largely achieved -with a clear majority of positives -and the criteria for granting a higher grade are not met,
grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met,
grade 2: not achieved for most of the cases covered by the verification.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WM-I-WMUM in USOSweb