Model syllabi for symbolic pattern recognition

The suggested syllabi is a result of surveying more than 80 courses related to various aspects of symbolic pattern recognition from more than 60 universities worldwide. Three courses with different lengths (short, medium and long) at two levels (undergraduate and postgraduate) are suggested. Although proposing a general syllabus for a symbolic pattern recognition course to cover various topics, which may needs different prerequisites, is nontrivial, hopefully the presented modules will help academics to prepare suitable course content more easily and faster.


	Overview Courses			Postgraduate Course
	Short	Medium	Long	Short	Medium	Long
Total Hours	10 hrs	20 hrs	40 hrs	10 hrs	20 hrs	40 hrs
1. General AI/Pattern Recognition	2	2	3	1	1	2
2. Repres. for Symbolic Reasoning	3	7	15	4	9	15
2-1. Blackboards	0	0	1	0	0	1
2-2. Frames	0	1	1	1	1	1
2-3. Semantic Nets	1	1	2	1	1	1
2-4. Decision Trees	0	1	1	0	0	1
2-5. Graphs	1	1	2	1	2	2
2-6. Languages, Formal and Informal	0	1	1	0	0	1
2-7. Predicate calculus	1	1	2	0	1	2
2-8. Production Rules	0	1	1	1	1	2
2-9. Modal Logic	0	0	2	0	2	2
2-10. Situation Calculus	0	0	2	0	1	2
3. Methods for Symbolic Reasoning	4	9	18	4	8	19
3-1. Logic Programming	0	2	3	0	0	2
3-2. Rule-based systems	1	1	2	1	1	2
3-3. Case-based Reasoning	0	1	2	0	1	2
3-4. Theorem Proving	1	1	2	1	1	2
3-5. Search	1	1	2	0	0	1
3-6. Parsing	0	1	2	1	1	2
3-7. Grammar Induction	0	0	2	0	1	2
3-8. Graph matching	1	1	1	0	1	2
3-9. Planning	0	1	2	1	2	4
4. Applications	1	2	4	1	2	4
4-1. Natural Language Processing	1	2	2	1	2	2
4-2. Multi-agent Systems	0	0	2	0	0	2

Suggested Syllabi for Courses on Symbolic Pattern Recognition