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 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 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 Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. X
2 Ability to formulate, and solve complex mechatronics engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. X
3 Ability to design a complex mechatronics engineering system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. X
4 Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in mechatronics engineering and robot technology practices; ability to employ information technologies effectively. X
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex mechatronics engineering and robot technology problems or research questions. X
6 Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7 Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8 Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 a-) Knowledge on behavior according to ethical principles, professional and ethical responsibility b-) Knowledge on standards used in engineering practices.
10 a-) Knowledge about business life practices such as project management, risk management, and change management b-) Awareness in entrepreneurship, innovation; knowledge about sustainable development.
11 Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
12 Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions in the field of mechatronics engineering.
13 Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planning, improving or changing the norms with a criticism.

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