- Nazwa przedmiotu:
- Artificial intelligence
- Koordynator przedmiotu:
- Włodzimierz Kasprzak, Ph.D., D.Sc. Professor
- Status przedmiotu:
- Obowiązkowy
- Poziom kształcenia:
- Studia II stopnia
- Program:
- Robotics
- Grupa przedmiotów:
- Przedmioty obowiązkowe
- Kod przedmiotu:
- EM11
- Semestr nominalny:
- 2 / rok ak. 2020/2021
- Liczba punktów ECTS:
- 4
- Liczba godzin pracy studenta związanych z osiągnięciem efektów uczenia się:
- 1) Number of hours that require the presence of a teacher - 50, including
a) presence of the lectures- 30;
b) presence in the exercises -15
c) presence on consultation - 5
2) The number of hours of independent work of student: 40
- Liczba punktów ECTS na zajęciach wymagających bezpośredniego udziału nauczycieli akademickich:
- 3 ECTS credits –
number of hours that require the presence of a teacher - 50, w including
a) presence of the lectures - 30,
b) presence in the exercises - 15,
c) presence on consultation - 5.
- Język prowadzenia zajęć:
- angielski
- Liczba punktów ECTS, którą student uzyskuje w ramach zajęć o charakterze praktycznym:
- 2 ECTS credits –
which are obtained during classes of a practical nature;
number of hours during classes of a practical nature - 50, including
b) presence in the exercises - 15
c) presence on consultation – 5
d) independent work of student on solving practical exercise tasks – 30
- Formy zajęć i ich wymiar w semestrze:
-
- Wykład30h
- Ćwiczenia15h
- Laboratorium0h
- Projekt0h
- Lekcje komputerowe0h
- Wymagania wstępne:
- x
- Limit liczby studentów:
- 100
- Cel przedmiotu:
- -
- Treści kształcenia:
- -
- Metody oceny:
- -
- Egzamin:
- tak
- Literatura:
- -
- Witryna www przedmiotu:
- http://studia.elka.pw.edu.pl/pub/14L/EAI.A/
- Uwagi:
- -
Efekty uczenia się
Profil ogólnoakademicki - wiedza
- Charakterystyka EM11_W1
- Students should be familiar with logical inference systems designed for perfect and imperfect knowledge representations.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_W04, AiR2_W07
Powiązane charakterystyki obszarowe:
I.P7S_WG, III.P7S_WG.o, P7U_W
- Charakterystyka EM11_W2
- Students should know state space search and agent action planning algorithms used in artificial intelligence.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_W07, AiR2_W04
Powiązane charakterystyki obszarowe:
P7U_W, I.P7S_WG, III.P7S_WG.o
- Charakterystyka EM11_W3
- Students should be familiar with knowledge representation systems and reasoning techniques.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_W04, AiR2_W07
Powiązane charakterystyki obszarowe:
I.P7S_WG, III.P7S_WG.o, P7U_W
- Charakterystyka EM11_W4
- Students should know machine learning techniques.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_W04, AiR2_W07
Powiązane charakterystyki obszarowe:
I.P7S_WG, III.P7S_WG.o, P7U_W
Profil ogólnoakademicki - umiejętności
- Charakterystyka EM11_U1
- Student should be able to design elements of autonomous agents.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_U01, AiR2_U06, AiR2_U16
Powiązane charakterystyki obszarowe:
P7U_U, I.P7S_UW.o, III.P7S_UW.o, I.P7S_UW, III.P7S_UW.2.o, III.P7S_UW.4.o, III.P7S_UW.1.o, III.P7S_UW.3.o
- Charakterystyka EM11_U2
- Student should be able to design knowledge-based systems, especially when implementing logical inference systems.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_U01, AiR2_U06
Powiązane charakterystyki obszarowe:
P7U_U, I.P7S_UW.o, III.P7S_UW.o, I.P7S_UW, III.P7S_UW.2.o, III.P7S_UW.4.o
- Charakterystyka EM11_U3
- Student should be able to deal with imperfect information, especially by designing fuzzy reasoning and probabilistic reasoning systems.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_U01, AiR2_U06
Powiązane charakterystyki obszarowe:
III.P7S_UW.o, I.P7S_UW, III.P7S_UW.2.o, III.P7S_UW.4.o, P7U_U, I.P7S_UW.o
- Charakterystyka EM11_U4
- Student should be able to solve agent’s activity control problems by advanced search and action planning algorithms.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_U01, AiR2_U06, AiR2_U16, AiR2_U17
Powiązane charakterystyki obszarowe:
P7U_U, I.P7S_UW.o, III.P7S_UW.o, I.P7S_UW, III.P7S_UW.2.o, III.P7S_UW.4.o, III.P7S_UW.1.o, III.P7S_UW.3.o
- Charakterystyka EM11_U5
- Student should be able to design machine learning algorithms (knowledge acquisition) by using: active observation, reinforcement learning and statistical learning.
Weryfikacja: Continuous assessment at tutorials regarding the acquired knowledge needed to solve computational and algorithmic exercise tasks, related to the content of this course.
Written assessment of the course outcomes by a written mid-time test.
Written assessment of the course outcomes by a final exam.
Powiązane charakterystyki kierunkowe:
AiR2_U01, AiR2_U06, AiR2_U16, AiR2_U17
Powiązane charakterystyki obszarowe:
P7U_U, I.P7S_UW.o, III.P7S_UW.o, I.P7S_UW, III.P7S_UW.2.o, III.P7S_UW.4.o, III.P7S_UW.1.o, III.P7S_UW.3.o