ECTS - Advanced Artificial Intelligence
Advanced Artificial Intelligence (CMPE568) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Advanced Artificial Intelligence | CMPE568 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| 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 and different approaches to Artificial Intelligence (AI) (including symbolic and non-symbolic ones). It also aims at extending the computer engineering vision of the student, and evaluating the possible research potentials of the students on the subject. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Intelligent agents, problem solving by searching, informed/uninformed search methods, exploration, constraint satisfaction problems, knowledge and reasoning, first-order logic, knowledge representation, learning, selected topics: neural networks, natural computing. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Intelligent Agents. Problem Solving by Searching, | Chapters 2-3.3 (main text) |
| 2 | Informed/Uninformed Search Methods, Exploration | Chapter 3.4-3.6 |
| 3 | Local search, search with non deterministic actions and partial observation | Chapter 4 |
| 4 | Adversarial Search and constraint satisfaction | Chapter 5,6 |
| 5 | Logical Agents and first order logic | Chapter 7,8 |
| 6 | Inference in first order logic | Chapter 9 |
| 7 | Planning and acting in real world | Chapter 10,11 |
| 8 | Knowledge representation | Chapter 12 |
| 9 | Uncertain Knowledge and Reasoning. Probabilistic reasoning | Chapter 13, 14, 15 |
| 10 | Making simple and complex Decisions | Chapter 16,17 |
| 11 | Learning from examples. Knowledge in learning | Chapter 18,19 |
| 12 | Learning probabilistic models. Reinforcement learning | Chapter 20,21 |
| 13 | Selected Topics | Chapter 23,24,25 |
| 14 | Selected Topics | Chapter 23,24,25 |
| 15 | Review | |
| 16 | Review |
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. Ant Colony Optimization, Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN: 0-262-04219-3. |
| 3. Artificial Intelligence, Patrick H. Winston, Addison-Wesley, 1992. ISBN: 0-201-533774. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 3 | 20 |
| Presentation | 1 | 15 |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 6 | 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 | Gains the ability to apply advanced computing and/or information knowledge in solving software engineering problems. | X | ||||
| 2 | Develops solutions using different technologies, software architectures and life-cycle approaches. | X | ||||
| 3 | Gains the ability to design, implement, and evaluate a software system, component, process, or program using modern techniques and engineering tools for software engineering practices. | X | ||||
| 4 | Gains ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | |||||
| 5 | Gains skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | |||||
| 6 | Gains the ability to access information to follow current developments in science and technology, conducts scientific research in the field of software engineering, and conducts a project. | |||||
| 7 | Acquires an understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Acquires project and risk management skills and gains awareness of the importance of entrepreneurship, innovation, and sustainable development, as well as international standards and methodologies. | |||||
| 9 | Understands the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Gains awareness of the development, adoption, and ongoing support for the use of excellence standards in software engineering practices. | |||||
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 | 16 | 1 | 16 |
| Presentation/Seminar Prepration | 1 | 10 | 10 |
| Project | |||
| Report | |||
| Homework Assignments | 3 | 6 | 18 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 15 | 15 |
| Prepration of Final Exams/Final Jury | 1 | 20 | 20 |
| Total Workload | 127 | ||