ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Computer Programming (Evening Education) | |||||
| Associate | TR-NQF-HE: Level 5 | QF-EHEA: Short Cycle | EQF-LLL: Level 5 | ||
Course Introduction and Application Information
| Course Code: | MYO046 | ||||
| Course Name: | Fundamentals of Artificial Intelligence | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | |||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Öğr. Gör. AHMET SELİM ÖVER | ||||
| Course Lecturer(s): | Öğr. Gör. Burak Ağgül | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | To understand the basic concepts and applications of artificial intelligence, to determine the learning algorithms with the appropriate optimization algorithm to be used in the analysis of the problems, to interprete the results obtained helping problems. |
| Course Content: | Introduction to artificial intelligence and basic concepts, problem analysis and solution, learning, different artificial intelligence algorithms, optimization algorithms, genetic algorithm. |
Learning Outcomes
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The students who have succeeded in this course;
1) To comprehend the basics of artificial neural network structures. 2) To comprehend the statistical learning. 3) To apply the difference machine learning algorithms. 4) To write a programs using artificial intelligence algorithms 5) To understand the optimization algorithms required for machine learning. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Artifical Intelligence | None |
| 2) | Artificial Neural Networks | None |
| 3) | Statistical Learning | None |
| 4) | Machine Learning | None |
| 5) | Deep Learning | None |
| 6) | Supervised Learning | None |
| 7) | Unsupervised Learning | None |
| 8) | MIDTERM EXAM | 1-7. Weeks |
| 9) | Reinforcement Learning | None |
| 10) | Natural Language Processing | None |
| 11) | Support Vector Machines | None |
| 12) | Computer Vision | None |
| 13) | Genetic Algorithm | None |
| 14) | Robotics | None |
| 15) | Final | 1-14. Weeks |
Sources
| Course Notes / Textbooks: | Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer. |
| References: | Wolfgang Ertel, Introduction to Artificial Intelligence, Second Edition, Springer. Charu C. Aggarwal, Neural Networks and Deep Learning, Springer. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Program Outcomes | |||||||||||||
| 1) He gains the ability of problem solving and analytical thinking skills. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 2) He learns the fundamentals of computer programming, hardware and software and the basic computer concepts. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 3) He develops algorithms according to the problems, gains the ability to distinguish the appropriate ones from the fundamental algorithms for the problem. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 4) He understands object-oriented programming concept and web programming. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 5) He learns radix systems, fundamental electronics and computer hardware knowledge. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 6) He gains mobile programming skills and develops applications for mobile platforms. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 7) He designs and codes databases. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 8) He learns to program and use computer networks, open source operating systems. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 9) He uses the English language effectively. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 10) He learns to use appropriate data structures according to programming requirements. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 11) He develops software individually or as a team. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 12) He follows developments in the field, high technology tools / applications. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 13) He gains awareness of professional and ethical responsibility and has an awareness of professional ethics. | 3 | 3 | 3 | 3 | 3 | ||||||||
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | He gains the ability of problem solving and analytical thinking skills. | 3 |
| 2) | He learns the fundamentals of computer programming, hardware and software and the basic computer concepts. | 3 |
| 3) | He develops algorithms according to the problems, gains the ability to distinguish the appropriate ones from the fundamental algorithms for the problem. | 3 |
| 4) | He understands object-oriented programming concept and web programming. | 3 |
| 5) | He learns radix systems, fundamental electronics and computer hardware knowledge. | 3 |
| 6) | He gains mobile programming skills and develops applications for mobile platforms. | 3 |
| 7) | He designs and codes databases. | 3 |
| 8) | He learns to program and use computer networks, open source operating systems. | 3 |
| 9) | He uses the English language effectively. | 3 |
| 10) | He learns to use appropriate data structures according to programming requirements. | 3 |
| 11) | He develops software individually or as a team. | 3 |
| 12) | He follows developments in the field, high technology tools / applications. | 3 |
| 13) | He gains awareness of professional and ethical responsibility and has an awareness of professional ethics. | 3 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 1 | % 20 |
| Midterms | 1 | % 30 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 3 | 7 | 2 | 27 | |||
| Homework Assignments | 1 | 5 | 10 | 15 | |||
| Midterms | 1 | 15 | 1 | 16 | |||
| Final | 1 | 20 | 1 | 21 | |||
| Total Workload | 79 | ||||||