ECTS - Applied Machine Learning in Data Analytics
Applied Machine Learning in Data Analytics (SE573) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Applied Machine Learning in Data Analytics | SE573 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
|
| Course Objectives | |
| Course Learning Outcomes |
The students who succeeded in this course; |
| Course Content | Data statistics; linear discriminant analysis; decision trees; artificial neural networks; Bayesian learning; distance measures; instance-based and reinforcement learning; clustering; regression; support vector machines. |
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 | To be able to use mathematics, science and engineering knowledge in solving engineering problems related to information systems. | X | ||||
| 2 | Design and conduct experiments in the field of informatics, analyze and interpret the results of experiments. | X | ||||
| 3 | Designs an information system, component and process according to the specified requirements. | X | ||||
| 4 | Can work effectively in disciplinary and multidisciplinary teams. | X | ||||
| 5 | Identify, formulate and solve engineering problems in the field of informatics. | |||||
| 6 | Acts in accordance with professional ethical rules. | |||||
| 7 | Communicates effectively both orally and in writing. | |||||
| 8 | Gains awareness of the necessity of lifelong learning. | |||||
| 9 | Learn about contemporary issues. | X | ||||
| 10 | To be able to use modern engineering tools, techniques and skills required for engineering practice. | X | ||||
| 11 | Knows project management methods and recognizes international standards. | X | ||||
| 12 | Develop informatics-related engineering products and prototypes for real-life problems. | X | ||||
| 13 | Contributes to professional knowledge. | |||||
| 14 | Can do methodological scientific research. | |||||
| 15 | Produce, report and present a scientific work based on an original or existing body of knowledge. | |||||
| 16 | Can defend the original idea generated. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | 8 | 2 | 16 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 4 | 8 |
| Prepration of Final Exams/Final Jury | 1 | 5 | 5 |
| Total Workload | 125 | ||