ECTS - Advanced Artificial Intelligence

Advanced Artificial Intelligence (CMPE568) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Artificial Intelligence CMPE568 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
Course Level Ph.D.
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
1 2 3 4 5
1 Ability to carry out advanced research activities, both individual and as a member of a team
2 Ability to evaluate research topics and comment with scientific reasoning
3 Ability to initiate and create new methodologies, implement them on novel research areas and topics
4 Ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions
5 Ability to apply scientific philosophy on analysis, modelling and design of engineering systems
6 Ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level
7 Contribute scientific and technological advancements on engineering domain of his/her interest area
8 Contribute industrial and scientific advancements to improve the society through research 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