Natural Language Processing WM-I-S2-E4-PJN

The Natural Language Processing (NLP) course is designed to equip students with the theoretical and practical knowledge necessary to understand and implement NLP techniques and models. This course covers a wide range of topics from basic text preprocessing to advanced deep learning models and large language models (LLMs). Students will gain hands-on experience through labs and projects, enabling them to apply NLP methods to real-world applications.

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

information and communication technology

(in Polish) E-Learning

(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

enter learning outcome code/codes

Type of subject

obligatory

Preliminary Requirements

* Basic knowledge of programming (Python preferred) *Understanding of fundamental concepts in machine learning *Familiarity with linear algebra, probability, and statistics

Course coordinators

Katsiaryna Kosarava

Learning outcomes

Lecture Outcomes:
W1: The student understands the theoretical and technical basis of NLP and various machine learning models applicable to text data.
U1: The student can apply NLP tools and methods in Python to solve domain-specific problems.

Lab Outcomes:
W1: The student understands the practical implementation of NLP techniques and can evaluate their performance.
U1: The student can implement and optimize NLP models using Python and relevant libraries.

Additional information

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

Description of WM-I-S2-E4-PJN in USOSweb