Advanced Artificial Intelligence (CMPE568) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Advanced Artificial Intelligence	CMPE568	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Natural & Applied Sciences Master's Degree
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	The objective of this course is to introduce basic concepts and different approaches to Artificial Intelligence (AI) (including symbolic and non-symbolic ones). It also aims at extending the computer engineering vision of the student, and evaluating the possible research potentials of the students on the subject.
Course Learning Outcomes	The students who succeeded in this course; Design an agent for a given problem Understand the problems and principles of searching for solution. Distinguish among variety of search algorithms. Comprehend first order logic and inference procedure in finding solutions to logical problems. Describe the fundamentals for machine learning.
Course Content	Intelligent agents, problem solving by searching, informed/uninformed search methods, exploration, constraint satisfaction problems, knowledge and reasoning, first-order logic, knowledge representation, learning, selected topics: neural networks, natural computing.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Intelligent Agents. Problem Solving by Searching,	Chapters 2-3.3 (main text)
2	Informed/Uninformed Search Methods, Exploration	Chapter 3.4-3.6
3	Local search, search with non deterministic actions and partial observation	Chapter 4
4	Adversarial Search and constraint satisfaction	Chapter 5,6
5	Logical Agents and first order logic	Chapter 7,8
6	Inference in first order logic	Chapter 9
7	Planning and acting in real world	Chapter 10,11
8	Knowledge representation	Chapter 12
9	Uncertain Knowledge and Reasoning. Probabilistic reasoning	Chapter 13, 14, 15
10	Making simple and complex Decisions	Chapter 16,17
11	Learning from examples. Knowledge in learning	Chapter 18,19
12	Learning probabilistic models. Reinforcement learning	Chapter 20,21
13	Selected Topics	Chapter 23,24,25
14	Selected Topics	Chapter 23,24,25
15	Review
16	Review

Sources

Course Book	1. Artificial Intelligence : A Modern Approach (Second Edition), Stuart Russell and Peter Norvig, Prentice-Hall, 2003, ISBN: 0-13-790395
Other Sources	2. Ant Colony Optimization, Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN: 0-262-04219-3.
	3. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	3	20
Presentation	1	15
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	25
Final Exam/Final Jury	1	40
Toplam	6	100

Percentage of Semester Work	60
Percentage of Final Work	40
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains the ability to have in-depth knowledge of mathematics, science, and engineering, and to use this knowledge in solving Civil Engineering problems.
2	Gains the ability to design and produce Civil Engineering systems under economic, environmental sustainability, and manufacturability constraints.
3	Gains the ability to identify, define, formulate, and solve complex engineering problems, and acquires the ability to select and apply appropriate analysis and modeling methods for this purpose.
4	Gains the ability to develop an approach to solve encountered engineering problems, and to design and conduct models and experiments.
5	Gains the ability to effectively use modern engineering tools, techniques, and capabilities necessary for design and other engineering applications.
6	Gains the ability to independently conduct fundamental research in the field, report research results effectively, and present them at scientific meetings.
7	Acquires sufficient verbal and written English skills to follow scientific developments in the field and to communicate with colleagues.
8	Gains the ability to effectively use the knowledge acquired in intra-disciplinary and interdisciplinary teams, and to take leadership roles in such teams.
9	Gains awareness of the necessity of lifelong learning, personal development, and continuous self-renewal in the field; follows developments in science and technology; acquires awareness of entrepreneurship and innovation.
10	Recognizes the importance of considering social, scientific, and ethical values in the stages of collecting, interpreting, disseminating, and applying data related to civil engineering problems.
11	Gains the competence to critically examine, develop, and, when necessary, take action to change social relations and the norms that govern them.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	1	16
Presentation/Seminar Prepration	1	10	10
Project
Report
Homework Assignments	3	6	18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	15	15
Prepration of Final Exams/Final Jury	1	20	20
Total Workload			127