Artificial Intelligence
EMARO course, Summer 2009
Contents:
Meeting times and locations
Lecture:
Tuesday, 13:15-15:00 (i.e. 1:15 p.m.- 3:00 p.m.) (two weeks out of three or every second week), room 520, E&IT Faculty
Exercises:
Tuesday, 13:15-15:00 (every third week), room 520, E&IT Faculty
[go to top]
Teaching staff and contact info
Professor Włodzimierz Kasprzak
Office: room 554, E&IT Faculty, Institute of Control and Computation Eng.
Office hours: Wednesday, 12-14 (i.e. 12 a.m.-2 p.m.)
22-234-7866, W.Kasprzak@ia.pw.edu.pl
[go to top],
Short course description
Course objectives
This course provides an introduction to artificial intelligence from the perspective
of robotics. Four parts are distinguished: logical systems, problem solving and planing, Bayesian networks
and machine learning.
Topics include: agent systems, inference, search, planing, Bayesian networks, dynamic Bayesian networks,
reinforcement learning, Bayesian learning.
Prerequisities
Students are expected to have the following background:
- knowledge at the level of BSc-studies in logic, probability theory, statistics and computer science -
in the area of programming, algorithmics and data structures.
- nowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial
computer program, preferably in one of the languages: C/C++, Java, C# or Pascal/Delphi.
Course materials
Lecture notes will be posted periodically on the course web site.
Selected chapters from the textbook below are recommended as optional reading.
[go to top]
Marks and Grading
Assessment will be marked out of a hundred. The marks equate to ECTS grades as given below:
|
ECTS Grade
|
A
|
B
|
C
|
D
|
E
|
F/FX
|
|
mark
|
100- 90
|
89-80
|
79-70
|
69-65
|
64-60
|
59 or less
|
Students are collecting assessment points. They come partly from a continuous assessment
in the semester time and from a final exam. The assessment method is:
- a midterm test for 0-40 pts.;
- a final exam for 0-60 pts.
In addition to satisfying the above assessment requirements, each
student must satisfy the attendance requirements.
There is an obligatory attendance of tutorial and an
optional attendance of the lecture.
The Pass mark for this course will be set at 60 pts.
Credits will be awarded to candidates who pass this course.
[go to top]
Lecture
Lecture schedule (20 h):
Tests:
- [29.04] Half-time test, 14.15 - 15.30. .
[go to top]
Textbooks and suggested readings
Textbooks:
- [RN] S.Russel, P.Norvig: Artificial Intelligence. A Modern Approach. Prentice Hall, Second Edition, 2002.
Supporting WWW page:
aima.cs.berkeley.edu
Suggested Readings
|
(Week) Topic
|
Readings
|
|
|
(Week 1)
L1.
|
[RN, ch. 1, 2]
|
|
|
(Week 2)
L2.
|
[RN, ch. 7,8 ]
|
|
|
(Week 3)
Tuto 1.
|
|
|
|
(Week 4)
Tuto 1 (cont.). L3.
|
[RN, ch. 9]
|
|
|
(Week 5)
L3 (cont.)
|
[RN, ch. 9]
|
|
|
(Week 6)
Tuto 2.
|
|
|
|
(Week 7)
L4.
|
[RN, ch. 3, 4]
|
|
|
(Week 8)
L5.
|
[RN, ch. 5]
|
|
|
(Week 9)
Tuto 3. Midterm test.
|
|
|
|
(Week 10)
L6.
|
[RN, ch. 11]
|
|
|
(Week 11)
L7.
|
[RN, ch. 13-15]
|
|
|
(Week 12)
Tuto 4. L8.
|
[RN, ch. 16]
|
|
|
(Week 13)
L9.
|
[RN, ch. 21]
|
|
|
(Week 14)
Tuto 5.
|
|
|
|
(Week 15)
5.06, Exam.
|
|
|
[go to top]
Tutorials/exercises
During the exercises some tasks will be solved,
which are related to the algorithms presented during the lecture.
Students will also select their homework problems.
The examination will cover smilar tasks as solved during exercises.
Exercises (12h)
- [12.03; 17.03] E1. Logical agents (AI-E1.pdf),
Exercises 1 (AI-E1-sol.pdf) with solutions
- [31.03] E2. Inference in logic. (AI-E2.pdf) ,
Exercises 2 (AI-E2-sol.pdf) with solutions
- [28.04] E3. Search. (AI-E3.pdf) ,
Exercises 3 (AI-E3-sol.pdf) with some solutions.
[29.04] Midterm test. 14.15-15.30
- [19.05] E4. Planning. Bayesian Networks. (AI-E4.pdf)
- [2.06] E5. Inference in BN. DBN. (AI-E5.pdf)
[5.06, time 12.15] Final test
[go to top]
Project work
On request.
[go to top]
W. Kasprzak.
Last modification:
12.06.2009.