Course Catalog 2010-2011
Basic

Basic Pori International Postgraduate Open University

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Course Catalog 2010-2011

OHJ-2556 Artificial Intelligence, 6 cr

Person responsible

Tapio Elomaa, Antti Valmari

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
Seminar



 



 
 4 h/week
 2 h/week

+2 h/week
+2 h/week
 2 h/week



 
OHJ-2556 2010-01 Tuesday 12 - 14, TB219
Thursday 12 - 14, TB223

Requirements

Weekly exercises, course work, and exam
Completion parts must belong to the same implementation

Learning outcomes

After completing the course the student will have be familiar with different areas of artificial intelligence. In particular, the student is able to apply to problems arising in application fields the basic methods of solving problems by searching, informed search and exploration, inference in first-order logic, probabilistic reasoning, learning from observations, and statistical learning methods. The student will be able to identify the computational complexity of the used technique. The student will obtain a deeper knowledge on the topic of the chosen study and presentation. Also hands-on experience on some specific artificial intelligence technique will be gathered in a programming home work.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Logic, knowledge, and reasoning  propositional and first-order logic  knowledge bases 
2. Problem solving and search  heuristic search  algorithm A* 
3. Uncertain knowledge and reasoning  probabilistic reasoning  decision making 
4. Machine learning  learning from observations  statistical learning 

Evaluation criteria for the course

The course grade will be based on the course exam, a study/presentation, and programming home work. Active participation to weekly exercises yields extra points. If the student demonstrates thorough understanding of the core content, s/he may pass the course with grade 3. In order to achieve grade 4, the student must also demonstrate competency in complementary knowledge. The student may achieve grade 5, if s/he also demonstrates good command of specialist knowledge. If there are minor shortcomings regarding the core content, the student may receive the grade 1 or 2, depending on the number of flaws. If there are significant shortcomings regarding core content, the student will not pass the course.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   Artificial Intelligence: A Modern Approach   S. Russell, P. Norvig   0-13-080302-2          English  

Prerequisites

Course Mandatory/Advisable Description
MAT-20501 Todennäköisyyslaskenta Advisable    
OHJ-2010 Tietorakenteiden käyttö Mandatory    

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
OHJ-2556 Artificial Intelligence, 6 cr OHJ-2550 Artificial Intelligence, 6 cr  

Additional information

Suitable for postgraduate studies

More precise information per implementation

Implementation Description Methods of instruction Implementation
OHJ-2556 2010-01 Spring 2011   Lectures
Seminar work
Excercises
Practical works
   
Contact teaching: 50 %
Distance learning: 0 %
Self-directed learning: 50 %  

Last modified19.08.2010