Computer Programming
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
Trade and Industry Vocational School
Programme of Computer Programming
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 03831190 | Machine Learning | 2 | Autumn | 3 | 2+1+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | Proje Tabanlı Öğrenme (PBL), Vaka Çalışmaları ve Gerçek Hayat Örnekleri |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Lect. Abubakar MAYANJA |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Abubakar MAYANJA | TSMYO-T213 | [email protected] | 7829 | Wednesday 11:00-12:00 |
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 | X |
| Specialization / Field Courses | X |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P11 | Creates algorithms and data structures and performs mathematical calculations. | 4 |
| P13 | Performs database design and management. | 5 |
| P14 | Tests software and fixes bugs. | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knows the basic elements of a computer. | P.1.1 | 1,7 |
| O2 | Knows how to use the internet and do research. | P.1.2 | 3,7 |
| O3 | Knows current techniques for data analysis. | P.3.1 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
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 | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 70 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 2 | 28 |
| Midterms | 1 | 15 | 15 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 14 | 1 | 14 |
| Laboratory | 14 | 1 | 14 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 15 | 15 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 128 | ||
| Total Work Load / 30 | 4,27 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P3 |
|---|---|---|---|
| O1 | Knows the basic elements of a computer. | 4 | - |
| O2 | Knows how to use the internet and do research. | 3 | - |
| O3 | Knows current techniques for data analysis. | - | 3 |