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Software Engineering
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CLICK HERE FOR THE COURSE SYLLABUS (.pdf)
COURSE INTRODUCTION AND APPLICATION INFORMATION
|
Course Name
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Laboratory
(hour/week) |
Local Credits
|
ECTS
|
|
Artificial Intelligence and Expert Systems
|
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) | |
| Course Assistants | - |
| Course Objectives | The goal of this course is to provide students with a survey of different aspects of Artificial Intelligence (AI). |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| 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. |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| 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 |
SOURCES
| Course Notes / 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 |
EVALUATION SYSTEM
| Semester Requirements | Number | Percentage of Grade |
| Attendance/Participation |
1
|
10
|
| Laboratory | ||
| Application | ||
| Field Work | ||
| Special Course Internship (Work Placement) | ||
| Quizzes/Studio Critics |
5
|
20
|
| Homework Assignments | ||
| Presentation/Jury |
2
|
40
|
| Project | ||
| Seminar/Workshop | ||
| Midterms/Oral Exams |
1
|
30
|
| Final/Oral Exam |
1
|
|
| Total |
| PERCENTAGE OF SEMESTER WORK | 10 |
100 |
| PERCENTAGE OF FINAL WORK | ||
| Total |
COURSE CATEGORY
|
Course Category |
Core Courses | |
| Major Area Courses | ||
| Supportive Courses |
X
|
|
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS
|
#
|
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
ECTS / WORKLOAD TABLE
| Activities | Number | Duration (Hours) | Total Workload |
| Course Hours (Including Exam Week: 16 x Total Hours) |
16
|
3
|
|
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class |
15
|
4
|
|
| Presentations / Seminar |
2
|
20
|
|
| Project | |||
| Homework Assignments | |||
| Quizzes |
5
|
8
|
|
| Midterms / Oral Exams |
1
|
37
|
|
| Final / Oral Exam | |||
| Total Workload |
CLICK HERE FOR THE COURSE SYLLABUS (.pdf)