ID2009 Artificial Intelligence: Principles and Techniques 7.5 credits
Artificial Intelligence: principer och tekniker
The course gives an introduction to the field of Artificial Intelligence
| Academic level (A-D) | C | Subject area | Information Technology |
| Educational level | 2 | Grade scale | A, B, C, D, E, FX, F |
Autumn 09 for single courses students
| Periods | 1 (7.5 cr) | Application code | 11507 |
| Start date | 28/08/2009 | End date | 24/10/2009 |
| Language of instruction | English | Campus | KTH Kista |
| Course pace | 100% | Tutoring time | Daytime |
| Number of places * | 1 - 40 | Form of study | NML |
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*)
Course may be cancelled if the number of applications is to few. Student may be denied admission if course is full. |
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| Course responsible | Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05 | ||
| Teacher | Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05 | ||
| Link to schedule (new window) | |||
Autumn 09 for programme students
| Periods | 1 (7.5 cr) | Application code | 70679 |
| Start date | End date | ||
| Language of instruction | English | Campus | KTH Kista |
| Course pace | 50% | Tutoring time | Daytime |
| Number of places | Form of study | NML | |
| Course responsible | Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05 | ||
| Teacher | Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05 | ||
| Link to schedule (new window) | |||
Autumn 09 for programme students
| Periods | 1 (7.5 cr) | Application code | 71551 |
| Start date | End date | ||
| Language of instruction | English | Campus | KTH Kista |
| Course pace | 50% | Tutoring time | Daytime |
| Number of places | Form of study | NML | |
| Course responsible | Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05 | ||
| Teacher | Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05 | ||
| Link to schedule (new window) | |||
Goals
Learning outcomes On successful completion of this course the student has:
Knowledge and understanding regarding:
- the objectives and the historical development of the field of artificial intelligence
- basic techniques for knowledge representation,
- basic techniques for automated reasoning, in particular search techniques and production systems
- basic techniques for machine learning
- the principles of symbolic programming
- major categories of applications of artificial intelligence techniques.
Skills and capacities, to be able to:
- design representations for particular problems, suitable for applying uninformed as well as informed search techniques
- apply uninformed as well as informed search techniques for particular problems
- model domain knowledge in terms of formal rules
- apply rule-based reasoning schemes to particular problems
- capture uncertain domain knowledge in representations
- implement problem solving schemes including representation and reasoning in terms of logic programming
- apply non-symbolic representation and reasoning schemes.
Values and attitudes, to be able to:
- compare the usefulness of alternative search techniques
- judge the validity and consistency of representations
- judge the validity of reasoning schemes with respect to particular problems
- compare symbolic and sub-symbolic approaches to problem solving.
Content
A brief description of course contents
• Introduction to the field of artificial intelligence including objectives, core technologies and applications.
• Introduction to knowledge representation including both symbolic and sub-symbolic approaches. Symbolic approaches include both logic and graph-based schemes while subsymbolic schemes include both connectionist and evolutionary computation representation schemes.
• Introduction to automated reasoning, including search techniques, production rule systems, connectionist and evolutionary computation reasoning schemes.•
Introduction to machine learning, including symbolic inductive learning techniques as well as connectionist and evolutionary computation learning schemes.
Disposition
The backbone of teaching consists of a series of lectures. The core of the course content is covered in four assignments, which are required to be solved and handed in on a weekly basis. The work on the assignments is supported by supervision in groups and individually.
Eligibility
For single course students not attending a regular KTH Programme the following is required:
- completed and documented upper secondary education including documented proficiency in English for applicants without knowledge of Swedish, shich is one of the general requisites for undergraduate studies in Sweden AND
- docuemtende university records corresponding to 180 hp/Bacherlor´s degree within Computer Science or equivalent.
Literature
Luger, George F.., “Artificial Intelligence Structures and Strategies for Complex Problem Solving, Addison Wesley.
Examination
- LAB1 - Assignment, 4.5 credits, grade scale: P, F
- TEN1 - Examination, 3.0 credits, grade scale: A, B, C, D, E, FX, F
Assessment on the course consists of four assignments (two individual and two performed in groups of two) and one written exam. To pass the whole course requires pass on both the assignments and the written examination.
Requirements for final grade
Lab work (LAB1; 4,5 hp), written exam (TEN1; 3 hp)
Offered by
ICT/Service Systems
Contact
Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05
Examiner
Carl-Gustaf Jansson, calle@dsv.su.se, 08-16 16 05
Version
Course plan valid from: Autumn 08.
