Nutrition and Dietetics
Course Details
KTO KARATAY UNIVERSITY
School of Health Sciences
Programme of Nutrition and Dietetics
Course Details
School of Health Sciences
Programme of Nutrition and Dietetics
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 99901047 | Artificial Intelligence and Machine Learning | 1 | Autumn | 1 | 2+0+0 | 3 | 3 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Most up-to-date books, magazines, articles and web links as well as videos and visuals on artificial intelligence and machine learning will be shared with students as the content of the course.
Objectives of the Course
Objectives of the course are providing students with knowledge about future technologies, equipping them with the know-how they will need to develop solution methods and strategies on artificial intelligence, machine learning and information technologies
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Historical development of information technologies, artificial intelligence and machine learning, general concepts |
| 2 | Introduction to artificial intelligence,intelligent agents |
| 3 | Introduction to solution of complex problems |
| 4 | Artificial neural networks – Supervised / unsupervised learning |
| 5 | Hybrid intelligent systems |
| 6 | Knowledge engineering |
| 7 | Data mining |
| 8 | Probabilistic reasoning |
| 9 | Making simple / complex decisions |
| 10 | Multiaging decision making, probabilistic programming |
| 11 | Learning from examples, learning probabilistic models |
| 12 | Deep Learning |
| 13 | Reinforcement learning, natural language processing |
| 14 | Deep learning for natural language processing |
| 15 | Robotics, computer vision, phiolosophy, ethics and safety of AI |
Textbook or Material
| Resources | Prolog Programming for Artificial Intelligence – Ivan Bratko |
| Prolog Programming for Artificial Intelligence – Ivan Bratko | |
| Prolog Programming for Artificial Intelligence – Ivan Bratko |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Midterms | - | - |
| Final Exam | - | - |
| Total | 0 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||