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 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
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 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 apply knowledge on Mathematics, Science and Engineering to advanced systems.
2 Implementing long-term research and development studies in the major fields of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications. X
4 Graduating researchers active on innovation and entrepreneurship.
5 Ability to report and present research results effectively.
6 Increasing the performance on accessing information resources and on following recent developments in science and technology.
7 An understanding of professional and ethical responsibility.
8 Increasing the performance on effective communications in both Turkish and English.
9 Increasing the performance on project management.
10 Ability to work successfully at project teams in interdisciplinary fields. X

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