Selected Applications of Artificial Intelligence Methods WM-I-S1-E6-SAAIM

The purpose of lectures is to present applications of artificial intelligence (AI) methods to solve selected real-life problems. The key idea is to consider problems of AI as either (a) problems of combinatorial optimization which can be solved with use of some metaheuristics such as e.g., genetic algorithms, or (b) problems which can be solved with use of neural networks, or (c) problems which can be interpreted in terms of multi-agent systems. Necessary AI tools will be presented, and it will be shown how to apply them for problems solving.

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

information and communication technology

(in Polish) E-Learning

(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

Lecture participation in classes: 30 hours presentation preparation: 15 hours preparation for verification: 25 hours consultations with the lecturer: 5 hours TOTAL: 75 hours, which corresponds to 3 ETCS Lab participation in classes: 30 hours solving homework: 10 hours project preparation: 30 hours consultations with the lecturer: 5 hours TOTAL: 75 hours, which corresponds to 3 ETCS

Subject level

intermediate

Learning outcome code/codes

LECTURES I1_W16, I1_U18 LABORATORY I1_W16, I1_U18

Type of subject

optional with unlimited choices

Preliminary Requirements

requested knowledge of probabilistic methods and ability of programmi

Course coordinators

Dorota Dąbrowska
Franciszek Seredyński

Learning outcomes

LECTURE
Student
W1-knows and understands the goals and scope of applications of artificial intelligence in other fields (I1_W16),
W2- knows selected methods of artificial intelligence (I1_W16),
U1 - can choose artificial intelligence methods for selected real problems (I1_U18).

LAB
Student
W1 - knows the terms used in selected artificial intelligence tools (I1_W16),
U1 - can apply selected tools and techniques of artificial intelligence to selected problems discussed in the lecture (I1_U18).

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-S1-E6-SAAIM in USOSweb