ECTSAdvanced Artificial Intelligence

Advanced Artificial Intelligence (MDES677) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Artificial Intelligence MDES677 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
Consent of the Instructor
Course Language English
Course Type Elective Courses Taken From Other Departments
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives To introduce advanced concepts and different approaches to Artificial Intelligence (AI) (including symbolic and non-symbolic ones). To extent the engineering vision of the student.
Course Learning Outcomes The students who succeeded in this course;
  • To learn how to design an agent for a given problem. To be able to decide on and apply suitable AI technique(s) to a given problem
Course Content Intelligent agents, problem solving by searching, informed/uninformed search methods, exploration, constraint satisfaction problems, game playing, knowledge and reasoning: first-order logic, knowledge representation, learning, selected topics: evolutionary computing, multiagent systems, artificial neural networks, ant colony optimization.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Intelligent Agents Chapters 1-2 from Russell & Norvig
2 Intelligent Agents Chapter 1-2 from Russell & Norvig
3 Informed/Uninformed Search Methods, Exploration Chapter 3-4 from Russell & Norvig
4 Informed/Uninformed Search Methods, Exploration Chapter 3-4 from Russell & Norvig
5 Constraint Satisfaction Problems Chapter 5 from Russell & Norvig
6 Constraint Satisfaction Problems Chapter 5 from Russell & Norvig
7 Game Playing Chapter 6 from Russell & Norvig
8 Knowledge and Reasoning : Logical Agents Chapter 7 from Russell & Norvig
9 Knowledge and Reasoning : First-Order Logic Chapter 8 from Russell & Norvig
10 Knowledge and Reasoning : Inference in First-Order Logic Chapter 9 from Russell & Norvig
11 Selected Topics : Evolutionary Computing Source #5
12 Selected Topics : Multiagent Systems Source #4
13 Selected Topics : Neural Networks Source #3
14 Selected Topics : At Colony Optimization Source #1
15 Overall review -
16 Final exam -

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.
3. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992.
4. Introduction to the Theory of Neural Computation, J. Hertz, A. Krogh and R.G. Palmer, Addison-Wesley Publishing Company, 1991
5. An Introduction to MultiAgent Systems, Wooldridge, M., John Wiley & Sons, 2002
6. An Introduction to Genetic Algorithms, Melanie Mitchell, MIT Press, 1998

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation 1 10
Project 1 25
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 25
Final Exam/Final Jury 1 40
Toplam 4 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses
Major Area Courses X
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
1 2 3 4 5
1 Gains the ability to understand and apply knowledge in the fields of mathematics, science and basic sciences at the level of expertise.
2 Gains the ability to access wide and deep knowledge in the field of Engineering by doing scientific research with current techniques and methods, evaluate, interpret and implement the gained knowledge.
3 Being aware of the latest developments his/her field of study, defines problems, formulates and develops new and/or original ideas and methods in solutions.
4 Designs and applies theoretical, experimental, and model-based research, analyzes and interprets the results obtained at the level of expertise.
5 Gains the ability to use the applications, techniques, modern tools and equipment in his/her field of study at the level of expertise.
6 Designs, executes and finalizes an original work process independently.
7 Can work in interdisciplinary and interdisciplinary teams, lead teams, use the information of different disciplines together and develop solution approaches.
8 Pays regard to scientific, social and ethical values in all professional activities and acquires responsibility consciousness at the level of expertise.
9 Contributes to the literature by communicating the processes and results of his/her academic studies in written form or orally in national and international academic environments, communicates effectively with communities and scientific staff working in the field of specialization.
10 Gains the skill of lifelong learning at the level of expertise.
11 Communicates verbally and in written form using a foreign language at least at the European Language Portfolio B2 General Level.
12 Recognizes the social, environmental, health, safety, legal aspects of engineering applications, as well as project management and business life practices, being aware of the limitations they place on engineering applications.

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 2 32
Presentation/Seminar Prepration 3 5 15
Project 1 20 20
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 8 8
Prepration of Final Exams/Final Jury 1 10 10
Total Workload 133