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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 |
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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:
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 |
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Additional information
Suitable for postgraduate studies
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Spring 2011 | Lectures Seminar work Excercises Practical works |
Contact teaching: 50 % Distance learning: 0 % Self-directed learning: 50 % |