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 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
(CMPE323 veya SE328)
Course Language English
Course Type Technical Elective Courses
Course Level Bachelor’s Degree (First Cycle)
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
Major Area Courses X
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 Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. X
4 The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively.
5 The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline. X
6 The ability to work effectively in inter/inner disciplinary teams; ability to work individually
7 Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions. X
8 The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
9 Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology. X
10 The ability to behave according to ethical principles, awareness of professional and ethical responsibility; X
11 Knowledge of the standards utilized in software engineering applications
12 Knowledge on business practices such as project management, risk management and change management;
13 Awareness about entrepreneurship, innovation
14 Knowledge on sustainable development
15 Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety;
16 Awareness of the legal consequences of engineering solutions
17 An ability to describe, analyze and design digital computing and representation systems. X
18 An ability to use appropriate computer engineering concepts and programming languages in solving computing problems. 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 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