1. THIRD CYCLE - DOCTORATE DEGREE
  2. THE GRADUATE SCHOOL OF SOCIAL SCIENCES
  3. INTERNATIONAL TRADE AND FINANCE DEPARTMENT
  4. 2987 INTERNATIONAL TRADE AND FINANCE PhD
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
UTFB 610BIG DATA AND MACHINE LEARNING3 + 02nd Semester10

COURSE DESCRIPTION
Course Level Doctorate Degree
Course Type Elective
Course Objective The aim of the course is to learn the concepts of artificial intelligence, big data and machine learning by applying problems.
Course Content The content of the course includes the concept of data manipulation, big data analytics, classification, prediction and regression analysis methods.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Understanding of data manipulation, big data analytics, classification, prediction and regression analysis

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04
LO 0014344
Sub Total4344
Contribution4344

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)1412168
Mid-terms11212
Final examination13838
Total Work Load

ECTS Credit of the Course





260

10
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2021-2022 Spring1UĞUR AKKOÇ


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
UTFB 610 BIG DATA AND MACHINE LEARNING 3 + 0 1 Turkish 2021-2022 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. UĞUR AKKOÇ uakkoc@pau.edu.tr İİBF A0301 %
Goals The aim of the course is to learn the concepts of artificial intelligence, big data and machine learning by applying problems.
Content The content of the course includes the concept of data manipulation, big data analytics, classification, prediction and regression analysis methods.
Topics
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes