Course Name | Artificial Intelligence and Expert Systems |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
SE 420 | Fall/Spring | 3 | 0 | 3 | 4 |
Prerequisites | None | |||||
Course Language | English | |||||
Course Type | Elective | |||||
Course Level | First Cycle | |||||
Course Coordinator | ||||||
Course Lecturer(s) | ||||||
Assistant(s) | - |
Course Objectives | The goal of this course is to provide students with a survey of different aspects of Artificial Intelligence (AI). |
Learning Outcomes | The students who succeeded in this course;
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Course Content | This course provides an introduction to Artificial Intelligence (AI). In this course we will study a number of theories, mathematical formalisms, and algorithms, that capture some of the core elements of computational intelligence. We will cover some of the following topics: search, logical representations and reasoning, automated planning, representing and reasoning with uncertainty, decision making under uncertainty, and learning. |
| Core Courses | |
Major Area Courses | ||
Supportive Courses | X | |
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Related Preparation |
1 | Introduction to AI Introduction to Expert Systems | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Ch 1Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
2 | Introduction to AI • Intelligent Agents Examples to AI Languages • Introduction to Common Lisp • Introduction to Prolog | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
3 | Problem Solving by Search I • Introduction to Search • Informed Search • Uninformed Search • Constraint Satisfaction Problems | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
4 | Problem Solving by Search II • Introduction to Search • Informed Search • Uninformed Search • Constraint Satisfaction Problems | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
5 | Problem Solving by Search III • Introduction to Search • Informed Search • Uninformed Search • Constraint Satisfaction Problems | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
6 | Expert Systems I | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
7 | Expert Systems II | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
8 | Expert Systems III | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
9 | MIDTERM | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
10 | AI: Language Processing | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
11 | AI: Machine Learning I | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
12 | AI: Machine Learning II | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
13 | AI: Information Retrieval | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
14 | Review of the Semester | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
15 | Review of the Semester | |
16 | Review of the Semester |
Course Textbooks | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN10: 0132124114. Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition |
References | Internet |
Semester Requirements | Number | Percentage |
Participation | 1 | 10 |
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | 5 | 20 |
Homework / Assignments | ||
Presentation / Jury | 2 | 40 |
Project | ||
Seminar / Workshop | ||
Midterms / Oral Exams | 1 | 30 |
Final / Oral Exam | 1 | |
Total |
Contribution of Semester Work to Final Grade | 10 | 100 |
Contribution of Final Work to Final Grade | ||
Total |
Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours Including exam week: 16 x total hours | 16 | 3 | 48 |
Laboratory / Application Hours Including exam week: 16 x total hours | 16 | ||
Study Hours Out of Class | 15 | 4 | |
Field Work | |||
Quizzes / Studio Critiques | 5 | 8 | |
Homework / Assignments | |||
Presentation / Jury | 2 | 20 | |
Project | |||
Seminar / Workshop | |||
Midterms / Oral Exams | 1 | 37 | |
Final / Oral Exam | 1 | ||
Total | 225 |
# | Program Qualifications / Outcomes | * Level of Contribution | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | Be able to define problems in real life by identifying functional and nonfunctional requirements that the software is to execute | X | ||||
2 | Be able to design and analyze software at component, subsystem, and software architecture level | X | ||||
3 | Be able to develop software by coding, verifying, doing unit testing and debugging | X | ||||
4 | Be able to verify software by testing its behaviour, execution conditions, and expected results | X | ||||
5 | Be able to maintain software due to working environment changes, new user demands and the emergence of software errors that occur during operation | X | ||||
6 | Be able to monitor and control changes in the software, the integration of software with other software systems, and plan to release software versions systematically | X | ||||
7 | To have knowledge in the area of software requirements understanding, process planning, output specification, resource planning, risk management and quality planning | X | ||||
8 | Be able to identify, evaluate, measure and manage changes in software development by applying software engineering processes | X | ||||
9 | Be able to use various tools and methods to do the software requirements, design, development, testing and maintenance | X | ||||
10 | To have knowledge of basic quality metrics, software life cycle processes, software quality, quality model characteristics, and be able to use them to develop, verify and test software | X | ||||
11 | To have knowledge in other disciplines that have common boundaries with software engineering such as computer engineering, management, mathematics, project management, quality management, software ergonomics and systems engineering | X | ||||
12 | Be able to grasp software engineering culture and concept of ethics, and have the basic information of applying them in the software engineering | X | ||||
13 | Be able to use a foreign language to follow related field publications and communicate with colleagues | X |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest