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2D1380 Artificial Intelligence

Course PM

The course gives a broad overview of the problems and methods studied in the field of artificial intelligence.

Timetable, Responsible, Eligibility, Aim, Course literature, Student office, Syllabus, Examination, Account atat NADA, Reading pointers

Any changes in the course PM will be posted under news.


Timetable for Lectures

Weekday Time Date Room
Friday 10-12 02.09 E2
Tuesday 10-12 06.09 E3
Friday 10-12 09.09 V2
Tuesday 10-12 13.09 E3
Friday 10-12 16.09 D3
Tuesday 10-12 20.09 E3
Friday 10-12 23.09 E2
Tuesday 10-12 27.09 L52
Friday 10-12 30.09 E2
Tuesday 10-12 04.10 E3
Friday 10-12 07.10 E2
Tuesday 10-12 11.10 E3

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Detailed Timetable for Homeworks, Course Assistance and Projects can be found here.

Course responsibility

Patric Jensfelt, Danica Kragic
Lecturers: Henrik I Christensen, Patric Jensfelt, Danica Kragic
Email: hic@nada.kth.se, patric@nada.kth.se, danik@nada.kth.se

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Prerequisites and Eligibility

The following or equivalent courses are a prerequisity:
2D1345 Introduction to Computer Science
2D1240 Numerical Methods, Basic Course
5B1506 Mathematical Statistics, Basic Course.

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Course aim

The aim of the course is to
* study methods for knowledge representation, data structures and algorithms useful
to enable students
* to develop systems that utilize artificial intelligence.

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Course material

Book

Artificial Intelligence: A Modern Approach (Second Edition) by Stuart J. Russell and Peter Norvig, Prentice Hall (2003), ISBN 0-13790-395-2

Lectures

All lectures will be accessible for download.

Student office and Delfi

NADAs student office address is Lindstedtsvägen 3, plan 2. It is opened Mo-Fr 9.45­-11.30 and Mo-Th 12.45-­14.15.

Delfi is NADAs system group that takes care of the computer accounts and access cards. Delfi is open Mo-Fr 10-12 and Mo-Th 13-15.

Syllabus

The following areas will be treated in the course: problem solving with search algorithms, heuristics and games, knowledge representation (logic), representing uncertain knowledge and reasoning (Bayesian networks), decision and utility theory, and machine learning. Examples of using artificial intelligence methods in computer vision, robotics, etc will be given.

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Examination

There will be two homework assignments and one big project assignment. There will be more information on these during the course.

For all examinations, we use the following code of honour..


 
Sidansvarig: Patric Jensfelt, Danica Kragic <patric@nada.kth.se, danik@nada.kth.se>
Uppdaterad: 2005-05-30