INFORMATION TECHNOLOGIES (ENGLISH, NON-THESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP5103 | Artificial Intelligence | Spring | 3 | 0 | 3 | 8 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Assist. Prof. TEVFİK AYTEKİN |
Course Lecturer(s): |
Prof. Dr. NAFİZ ARICA |
Recommended Optional Program Components: | None |
Course Objectives: | The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems. |
The students who have succeeded in this course; I. Be able to solve problems by applying a suitable search method. II. Be able to implement minimax search and alpha-beta pruning in game playing. III. Be able to use logical formalisms in modeling. IV. Be able to apply supervised learning techniques to a given problem. V. Be able to apply unsupervised learning techniques to a given problem. VI. Be able to use the basic techniques in natural language processing. |
introduction; uninformed search strategies; informed (heuristic) search strategies; adversarial search; propositional logic; predicate logic; supervised learning techniques; unsupervised learning techniques; natural language processing. |
Week | Subject | Related Preparation |
1) | Introduction | |
2) | Uninformed Search Strategies | |
3) | Uninformed Search Strategies | |
4) | Informed (Heuristic) Search Strategies | |
5) | Informed (Heuristic) Search Strategies | |
6) | Adversarial Search | |
7) | Propositional Logic | |
8) | Predicate logic | |
9) | Supervised Learning Techniques | |
10) | Supervised Learning Teknileri | |
11) | Unsupervised Learning Techniques | |
12) | Unsupervised Learning Techniques | |
13) | Natural Language Processing | |
14) | Natural Language Processing | |
15) | Final Exam |
Course Notes / Textbooks: | Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009. Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004. |
References: |
Semester Requirements | Number of Activities | Level of Contribution |
Presentation | 1 | % 10 |
Project | 1 | % 50 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 10 | |
PERCENTAGE OF FINAL WORK | % 90 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 14 | 3 | 42 |
Project | 1 | 42 | 42 |
Homework Assignments | 7 | 8 | 56 |
Final | 1 | 12 | 12 |
Total Workload | 194 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Uses basic Software Engineering knowledge and competencies. | |
2) | Applies the software development ability that is necessary for software engineering applications. | |
3) | Uses data structures and applies information about algorithm development. | |
4) | Develops system programs on operating systems. | |
5) | Defines computer organization, design and architectures. | |
6) | Creates the structure of computer networks and network security. | |
7) | Uses business intelligence, data mining and data analysis tools, applies techniques about them. | |
8) | Develops database applications and WEB based programs. | |
9) | Defines, analyzes, designs and manages information technologies projects. | |
10) | Uses and develops technology-based environments and tools in education. | |
11) | Detects, identifies and solves information technology needs of the business environment. | |
12) | Uses the capabilities of information technologies within the rules of professional responsibility and ethics. |