ECTS - Advanced 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 3 0 0 3 5
Pre-requisite Course(s)
Consent of the Instructor
Course Language English
Course Type N/A
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 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 -


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 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
1 2 3 4 5
1 Ability to expand and get in-depth information with scientific researches in the field of mechanical engineering, evaluate information, review and implement.
2 Have comprehensive knowledge about current techniques and methods and their limitations in Mechanical engineering.
3 To complete and apply knowledge by using scientific methods using uncertain, limited or incomplete data; use information from different disciplines.
4 Being aware of the new and developing practices of Mechanical Engineering and being able to examine and learn when needed.
5 Ability to define and formulate problems related to Mechanical Engineering and develop methods for solving and apply innovative methods in solutions.
6 Ability to develop new and/or original ideas and methods; design complex systems or processes and develop innovative/alternative solutions in the designs.
7 Ability to design and apply theoretical, experimental and modeling based researches; analyze and solve complex problems encountered in this process.
8 Work effectively in disciplinary and multi-disciplinary teams, lead leadership in such teams and develop solution approaches in complex situations; work independently and take responsibility.
9 To establish oral and written communication by using a foreign language at least at the level of European Language Portfolio B2 General Level.
10 Ability to convey the process and results of their studies systematically and clearly in written and oral form in national and international environments.
11 To know the social, environmental, health, security, law dimensions, project management and business life applications of engineering applications and to be aware of the constraints of their engineering applications.
12 Ability to observe social, scientific and ethical values in the stages of data collection, interpretation and announcement and 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
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration 3 5 15
Project 1 20 20
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