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 | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | The objective of this course is to introduce basic concepts in both single, multi agent and swarm intelligence approaches to Artificial Intelligence (AI). |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| 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 (Russel & Norvig) |
| 2 | Agent Paradigm | Chapters 1-2 (Russel & Norvig) |
| 3 | Problem Solving by Searching, | Chapter 3 (Russel & Norvig) |
| 4 | Informed/Uninformed Search Methods | Chapter 4 (Russel & Norvig) |
| 5 | Genetic Algorithms and Simulated Annealing | Chapter 4 (Russel & Norvig) |
| 6 | Adversarial Search | Chapter 5 (Russel & Norvig) |
| 7 | Constraint Satisfaction Problems | Chapter 6 (Russel & Norvig) |
| 8 | Constraint Satisfaction Problems | Chapter 6 (Russel & Norvig) |
| 9 | Swarm Intelligence: Particle Swarm Optimization | Chapter 5.4 (de Castro) |
| 10 | Swarm Intelligence: Artificial Bee Colony Optimization | Chapter 9 (Karaboğa) |
| 11 | Swarm Intelligence: Ant Colony Optimization | Chapter 5.2 (de Castro) |
| 12 | Swarm Intelligence: Ant Colony Optimization | Chapter 5.2 (de Castro) |
| 13 | Multi-Agent Systems & Intelligent Agents | Chapters 1-2 (Wooldridge) |
| 14 | Multi-Agent Systems & Intelligent Agents | Chapters 1-2 (Wooldridge) |
| 15 | Multi-Agent Interactions | Chapter 11 (Wooldridge) |
| 16 | Philosophical Foundations & Ethics | Chapter 27 (Russel & Norvig) |
Sources
| Course Book | 1. Artificial Intelligence: A Modern Approach (Fourth Edition). Stuart Russell and Peter Norvig Pearson Education, 2020, ISBN-13 : 978-1292153964 |
|---|---|
| Other Sources | 2. https://aima.cs.berkeley.edu |
| 3. L.N. de Castro, Fundamentals of Natural Computing: Basic Concepts, Algorithms and Applications, Chapman & Hall/CRC, 2006. ISBN # 1-58488-643-9. | |
| 4. D.Karaboğa, “Yapay Zeka Optimizasyon Algoritmaları”, Nobel Akademik Yayıncılık, 2014, ISBN: 9786051337647 (in Turkish) | |
| 5. M. Wooldridge, An Introduction to Multi-Agent Systems, Wiley, 2009, ISBN-13 : 978-0470519462 | |
| 6. M. Dorigo and T. Stützle, “Ant Colony Optimization”, MIT Press, 2004, ISBN # 0-262-04219-3. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 2 | 20 |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 35 |
| Final Exam/Final Jury | 1 | 45 |
| Toplam | 4 | 100 |
| Percentage of Semester Work | 55 |
|---|---|
| Percentage of Final Work | 45 |
| 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 | Applies knowledge of mathematics, science, and engineering | X | ||||
| 2 | Designs and conducts experiments, analyzes and interprets experimental results. | X | ||||
| 3 | Designs a system, component, or process to meet specified requirements. | X | ||||
| 4 | Works effectively in interdisciplinary fields. | |||||
| 5 | Identifies, formulates, and solves engineering problems. | X | ||||
| 6 | Has awareness of professional and ethical responsibility. | |||||
| 7 | Communicates effectively. | |||||
| 8 | Recognizes the need for lifelong learning and engages in it. | X | ||||
| 9 | Has knowledge of contemporary issues. | X | ||||
| 10 | Uses modern tools, techniques, and skills necessary for engineering applications. | X | ||||
| 11 | Has knowledge of project management skills and international standards and methodologies. | |||||
| 12 | Develops engineering products and prototypes for real-world problems. | X | ||||
| 13 | Contributes to professional knowledge. | X | ||||
| 14 | Conducts methodological and scientific research. | X | ||||
| 15 | Produces, reports, and presents a scientific work based on original or existing knowledge. | X | ||||
| 16 | Defends the original idea generated. | |||||
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 | 2 | 10 | 20 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 12 | 12 |
| Prepration of Final Exams/Final Jury | 1 | 18 | 18 |
| Total Workload | 126 | ||