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 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 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 Having accumulated knowledge on mathematics, science and engineering and an ability to apply these knowledge to solve Civil engineering problems.
2 Ability to design Cİvil Engineering systems fulfilling sustainability in environment and manufacturability and economic constraints
3 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems.
4 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment
5 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications
6 Ability to carry out independent research in the field and to report the results of the research effectively and be able to present the research results at scientific meetings.
7 Sufficient oral and written English knowledge to follow scientific conferences in the field and communicate with colleagues.
8 Ability to effectively use knowledge in the field to work in disciplinary/multidisciplinary teams and the skill to lead these teams
9 Consciousness on the necessity of improvement and sustainability as a result of life-long learning,ability for continuous renovation and monitoring the developments on science and technology and awareness on entrepreneurship and innovation
10 Professional and ethical responsibility to gather and interpret data, apply and announce solutions to Civil Engineering problems.
11 An ability to investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary.

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