ECTS - Introduction to Artificial Intelligence

Introduction to Artificial Intelligence (CMPE462) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Introduction to Artificial Intelligence CMPE462 3 0 0 3 5
Pre-requisite Course(s)
CMPE323 Algoritma
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 The objective of this course is to introduce basic concepts in both symbolic and non-symbolic approaches to Artificial Intelligence (AI).
Course Learning Outcomes The students who succeeded in this course;
  • To understand agent paradigm and its relation to AI.
  • To practice basic AI technique(s) and algorithms to different problem domains.
Course Content Agent Paradigm, Problem Solving by Searching, Informed/Uninformed Search Methods, Genetic Algorithms, Simulated Annealing, Constraint Satisfaction Problems, Adversarial Search, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization, Multi-Agent Systems & Intelligent Agents, Multi-Agent Interactions, Philosophical Foundations & Ethics.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Agent Paradigm Chapters 1-2 (main text)
2 Agent Paradigm Chapter 1-2
3 Problem Solving by Searching, Ch 3
4 Informed/Uninformed Search Methods Ch. 4
5 Genetic Algorithms and Simulated Annealing Ch. 4
6 Constraint satisfaction problems Ch. 5
7 Adversarial Search Ch. 6
8 Logical Agents Ch. 7
9 Knowledge Engineering Resource #5
10 Expert Systems Resource #4
11 Expert Systems Resource #4
12 Communication Ch. 22
13 Communication Ch. 22
14 AI Applications Resource #3

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. 1. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774.
3. 2. http://www.cs.rmit.edu.au/AI-Search/Product/
4. 3. “Engineering Applications of Artificial Intelligence” journal, ISSN: 0952-1976, Elsevier, B.V.
5. 4. Expert Systems: Principles and Programming, Fourth Edition by Joseph C. Giarratano and Gary D. Riley, PWS Publishing Company, 2004.
6. 5. Knowledge Representation and Reasoning, Ronald Brachman and Hector Levesque, The Morgan Kaufmann Series in Artificial Intelligence , 2004.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 3 35
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 25
Final Exam/Final Jury 1 40
Toplam 5 100
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 An ability to apply knowledge of mathematics, science, and engineering.
2 An ability to design and conduct experiments, as well as to analyse and interpret data.
3 An ability to design a system, component, or process to meet desired needs.
4 An ability to function on multi-disciplinary domains.
5 An ability to identify, formulate, and solve engineering problems.
6 An understanding of professional and ethical responsibility.
7 An ability to communicate effectively.
8 Recognition of the need for, and an ability to engage in life-long learning.
9 A knowledge of contemporary issues.
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
11 Skills in project management and recognition of international standards and methodologies
12 An ability to produce engineering products or prototypes that solve real-life problems.
13 Skills that contribute to professional knowledge.
14 An ability to make methodological scientific research.
15 An ability to produce, report and present an original or known scientific body of knowledge.
16 An ability to defend an originally produced idea.

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 14 2 28
Presentation/Seminar Prepration
Project
Report
Homework Assignments 3 8 24
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 15 15
Total Workload 125