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 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Computer Engineering 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
Major Area Courses
Supportive Courses X
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 An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. X
2 Develop solutions using different technologies, software architectures and life-cycle approaches. X
3 An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. X
4 An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements.
5 Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects.
6 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
7 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
9 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

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