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 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 Gains the ability to have in-depth knowledge of mathematics, science, and engineering, and to use this knowledge in solving Civil Engineering problems.
2 Gains the ability to design and produce Civil Engineering systems under economic, environmental sustainability, and manufacturability constraints.
3 Gains the ability to identify, define, formulate, and solve complex engineering problems, and acquires the ability to select and apply appropriate analysis and modeling methods for this purpose.
4 Gains the ability to develop an approach to solve encountered engineering problems, and to design and conduct models and experiments.
5 Gains the ability to effectively use modern engineering tools, techniques, and capabilities necessary for design and other engineering applications. X
6 Gains the ability to independently conduct fundamental research in the field, report research results effectively, and present them at scientific meetings.
7 Acquires sufficient verbal and written English skills to follow scientific developments in the field and to communicate with colleagues. X
8 Gains the ability to effectively use the knowledge acquired in intra-disciplinary and interdisciplinary teams, and to take leadership roles in such teams.
9 Gains awareness of the necessity of lifelong learning, personal development, and continuous self-renewal in the field; follows developments in science and technology; acquires awareness of entrepreneurship and innovation. X
10 Recognizes the importance of considering social, scientific, and ethical values in the stages of collecting, interpreting, disseminating, and applying data related to civil engineering problems.
11 Gains the competence to critically examine, develop, and, when necessary, take action to change social relations and the norms that govern them.

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