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 Ph.D.
Mode of Delivery
Learning and Teaching Strategies .
Course Coordinator
Course Lecturer(s)
Course Assistants
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 Comprehends the most advanced technology and literature in the field of software engineering research. X
2 Gains the ability to conduct world-class research in software engineering and publish scholarly articles in top conferences and journals in the area. X
3 Conducts quantitative and qualitative studies in software engineering.
4 Develops and applies software engineering approaches to acquire the necessary skills to bridge the gap between academia and industry in the field of software engineering and to solve real-world problems.
5 Gains the ability to access the necessary information to follow current developments in science and technology, and to conduct scientific research or develop projects in the field of software engineering.
6 Gains awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of software engineering.
7 Acquires project and risk management skills; gains awareness of the importance of entrepreneurship, innovation, and sustainable development; adapts international excellence standards for software engineering practices and methodologies.
8 Gains awareness of the universal, environmental, social, and legal consequences of software engineering practices when making decisions.
9 Develops, adopts, and supports the sustainable use of excellence standards for software engineering practices.

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