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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
UTFB 512INDUSTRY AND FIELDWORK3 + 02nd Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective The aim of the course is to ensure that graduate students closely examine the activities of exporting firms and carry out field study on sectoral basis.
Course Content It is also intended to develop the perspective of the students about the companies' foreign trade processes by case studies, make company visits, and analyze the real data of the firms.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Students will be able to get a closer look at the companies, the general operation and the problems faced by the companies with field studies.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03
LO 001334
Sub Total334
Contribution334

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)148112
Mid-terms11111
Final examination13030
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2019-2020 Spring1EDA YALÇIN KAYACAN


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
UTFB 512 INDUSTRY AND FIELDWORK 3 + 0 1 Turkish 2019-2020 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Assoc. Prof. Dr. EDA YALÇIN KAYACAN eyalcin@pau.edu.tr İİBF B0213 İİBF B0214 %70
Goals The aim of the course is to ensure that graduate students closely examine the activities of exporting firms and carry out field study on sectoral basis.
Content It is also intended to develop the perspective of the students about the companies' foreign trade processes by case studies, make company visits, and analyze the real data of the firms.
Topics
WeeksTopics
1 What is Data Science?The Concepts of Data Science, Artificial Intelligence, Artificial Neural Networks, Machine Learning and Deep Learning
2 Introduction to Python: setup and data structures (list, dictionary, series)
3 Programming in Python: functions & loops
4 Data Visualization with Python
5 Basic Statistics with Python
6 Data Preprocessing with Python
7 The Basics of Machine Learning
8 Midterm
9 Machine Learning: Supervised, Unsupervised Learning
10 Machine Learning
11 Mathematical Basics of Deep Learning
12 Deep Learning: Convolutional Neural Networks
13 Deep Learning: Recurrent Neural Networks
14 Deep Learning: LSTM
Materials
Materials are not specified.
Resources
ResourcesResources Language
Derin Öğrenme: Ian Goodfellow, Yoshua Bengio & Aaron Courville. Buzdağı Yayıncılık. Türkçe
Yapay Zeka Uygulamaları: Prof. Dr. Çetin ElmasTürkçe
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