Artificial Intellinge

Course Code:

ACM465

Course Period:

Autumn

Course Type:

Area Elective

P:

3

Lab:

0

Credits:

3

ECTS:

6

Course Language:

İngilizce

Course Objectives:

Introduction, programming language: LISP: array, tree, heap, queue and table structures, information display: production rules including hierarchies, propositional account, inference rules, frames, semantic networks, restrictions and systematical approaches, search, hypothesis and testing, depth first search, width first search, intuitional search, optimal search, game trees and reflexive search, mini max search, alpha-beta reduction, learning description trees, artifical neural networks, perceptions, genetic algorithms, expert systems, natural language process, speech recognition, computer vision.

Course Content:

Introduction, programming language: LISP: array, tree, heap, queue and table structures, information display: production rules including hierarchies, propositional account, inference rules, frames, semantic networks, restrictions and systematical approaches, search, hypothesis and testing, depth first search, width first search, intuitional search, optimal search, game trees and reflexive search, mini max search, alpha-beta reduction, learning description trees, artifical neural networks, perceptions, genetic algorithms, expert systems, natural language process, speech recognition, computer vision.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Discussion, 4: Lab Work

Course Evaluation Methods:

A: Testing, B: Laboratory C: Homework D: Project

Vertical Tabs

Course Learning Outcomes

Learning Outcomes		Program Learning Outcomes	Teaching Methods			Assessment Methods
Developing a variety of approaches with general applicability.	3,6,9			1,3,4	A,B,C
Acquire a working knowledge of the LISP language, its procedural and data structures	2,3,6,9			1,2,3,4	A,B,C
Understand and implement AI search models and generic search strategies	3,6,9			1,3,4	A,B,C
Use probability as a mechanism for handling uncertainty in AI.	2,6,9			1,3,4	A,B,C
Understand the design of AI systems involving learning to enhance performance.	3,6,9			1,3,4	A,B,C,D
Logic and its application as a form of representing knowledge in AI systems	3,9,6			1,2,3,4	A,B,C,D
Introducing specific applications such as computer vision, atural language processing, expert systems,	3,9			1,2,3,4	A,B,C,D

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction, history	ACM 221
2	The LISP programming language	ACM 361
3	LAB: Program and data structures in LISP.	ACM 369
4	Intelligent agents.	ACM 366
5	Problem solving, uninformed search	ACM 361,369
6	Search and heuristic functions, Local search, Online search,	ACM 111
7	MIDTERM EXAMINATION
8	Constraint satisfaction	ACM 111
9	Game playing,	ACM 369
10	Logical agents; propositional logic, Inference in propositional logic,	ACM 363
11	First order logic, Inference in first order logic,	ACM 361
12	LAB: logic programming,	ACM 361
13	Planning problems,	ACM 370
14	Expert Systems	ACM 369
15	REVIEW AND MIDTERM EXAMINATION

Recommended Sources

RECOMMENDED SOURCES
Textbook	Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Prentice Hall ISBN-13; 978-0-13-604259-4 (2010)
Additional Resources	Peter Norvig, Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp An Imprint of Elsevier. Morgan Kaufmann Publishers San Francisco, CA

Material Sharing

MATERIAL SHARING
Documents	Presentations and Laboratory Sheets
Assignments	Homework Sheets
Exams	Old exam questions are furnished

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	2	66
Quizzes	4	16
Assignment and Labwork	10	18
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	3	48
Mid-terms	2	2	4
Quizzes	4	1	4
Homework	10	3	30
Final examination	2 (Including reparation)	2	4
Total Work Load			138
Total Work Load / 25 (h)			5.52
ECTS Credit of the Course			6

None

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