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	Attains knowledge through wide and in-depth investigations his/her field and surveys, evaluates, interprets, and applies the knowledge thus acquired.
2	Has a critical and comprehensive knowledge of contemporary engineering techniques and methods of application.
3	By using unfamiliar, ambiguous, or incompletely defined data, completes and utilizes the required knowledge by scientific methods; is able to fuse and make use of knowledge from different disciplines.
4	Has the awareness of new and emerging technologies in his/her branch of engineering profession, studies and learns these when needed.
5	Defines and formulates problems in his/her branch of engineering, develops methods of solution, and applies innovative methods of solution.
6	Devises new and/or original ideas and methods; designs complex systems and processes and proposes innovative/alternative solutions for their design.
7	Has the ability to design and conduct theoretical, experimental, and model-based investigations; is able to use judgment to solve complex problems that may be faced in this process.
8	Functions effectively as a member or as a leader in teams that may be interdisciplinary, devises approaches of solving complex situations, can work independently and can assume responsibility.
9	Has the oral and written communication skills in one foreign language at the B2 general level of European Language Portfolio.
10	Can present the progress and the results of his investigations clearly and systematically in national or international contexts both orally and in writing.
11	Knows social, environmental, health, safety, and legal dimensions of engineering applications as well as project management and business practices; and is aware of the limitations and the responsibilities these impose on engineering practices.
12	Commits to social, scientific, and professional ethics during data acquisition, interpretation, and publication as well as in all professional activities.

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