ECTS - Machine Learning
Machine Learning (IKT484) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Machine Learning | IKT484 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
|
| Course Objectives | |
| Course Learning Outcomes |
The students who succeeded in this course; |
| Course Content | The main contents are, supervised learning unsupervised learning ; learning theory ; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing and evaluation of policies and programs. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|
Sources
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | - | - |
| Final Exam/Final Jury | - | - |
| Toplam | 0 | 0 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| 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 | They acquire the skills to understand, explain, and use the basic concepts and methods of economics. | |||||
| 2 | Acquires macro-economic analysis skills. | |||||
| 3 | Acquire microeconomic analysis skills. | |||||
| 4 | Understands the formulation and implementation of economic policies at local, national, regional and/or global levels. | |||||
| 5 | Learn different approaches to the economy and economic issues. | |||||
| 6 | Learn qualitative and quantitative research techniques in economic analysis. | |||||
| 7 | Improving the ability to use modern software, hardware and/or other technological tools. | |||||
| 8 | Develops intra-disciplinary and inter-disciplinary team work skills. | |||||
| 9 | Contributes to open-mindedness by encouraging critical analysis, discussion, and/or lifelong learning. | |||||
| 10 | Develops a sense of work ethics and social responsibility. | |||||
| 11 | Develops communication skills. | |||||
| 12 | Improving the ability to effectively apply knowledge and skills in at least one of the following areas: Economic policy, public policy, international economic relations, industrial relations, monetary and financial relations | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | |||
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | |||
| Prepration of Final Exams/Final Jury | |||
| Total Workload | 0 | ||