ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Management Information Systems | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
Course Introduction and Application Information
| Course Code: | YBS022 | ||||
| Course Name: | Deep Learning 1 | ||||
| Semester: | Spring | ||||
| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | Yalçın Özkan | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | This course aims to teach the fundamentals of machine learning and deep learning algorithms, develop applications using the python programming language, and teach the methods of improving the performance of applications. |
| Course Content: | This course includes the mathematical principles of the deep learning algorithm, deep learning libraries, the use of the Keras library, and various applications of deep learning models. |
Learning Outcomes
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The students who have succeeded in this course;
1) Students will comprehend the processes of machine learning 2) Understand the use of Python language more effectively in machine learning 3) Get the opportunity to get to know deep learning libraries more closely 4) Understand classification and regression models with the help of deep learning algorithms 5) Learn about the use of deep learning algorithms in various sectors |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to machine learning | |
| 2) | Mathematical fundamentals of the deep learning algorithm | |
| 3) | Preprocessing Methods | |
| 4) | Classification process, classification concepts, Deep learning libraries | |
| 5) | Classification application with deep learning-1 | |
| 6) | Classification application with deep learning-2 | |
| 7) | Classification application with deep learning-3 | |
| 8) | Midterm | |
| 9) | Recurrent deep learning algorithms | |
| 10) | Classification with LSTM deep networks | |
| 11) | Regression with LSTM deep networks | |
| 12) | Deep autocoders | |
| 13) | Summarizing data with autocoders | |
| 14) | Data summarization application with autocoders | |
| 15) | Classification of unbalanced data with autocoders | |
| 16) | Final Exam Period |
Sources
| Course Notes / Textbooks: | Ian Goodfellow, yoshua Bengio, Aaron Courville, (2018), “Derin Öğrenme”, Buzdağı Yayınevi. François Chollet, (2019), “Python ile Derin Öğrenme”, Buzdağı Yayınevi. |
| References: | Ek kaynak ihtiyacı bulunmamaktadır. - There is no need for additional resources. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Program Outcomes | |||||||||||||
| 1) It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering. | 3 | 2 | 3 | 3 | 3 | ||||||||
| 2) Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 3) Uses different information technologies and systems for understanding and solving various business problems. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 4) Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods. | 3 | 3 | 3 | 3 | 3 | ||||||||
| 5) Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database. | 3 | 3 | 3 | 2 | 3 | ||||||||
| 6) Gains technical and managerial contributions to IT projects and takes responsibility. | 3 | 2 | 3 | 3 | 3 | ||||||||
| 7) Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively. | 3 | 3 | 3 | 2 | 3 | ||||||||
| 8) Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education. | 2 | 2 | 2 | 2 | 2 | ||||||||
| 9) Develops teamwork, negotiation, leadership and entrepreneurship skills. | 2 | 2 | 3 | 2 | 2 | ||||||||
| 10) Has universal ethical values, social responsibility awareness and sufficient legal knowledge. | 2 | 2 | 2 | 2 | |||||||||
| 11) Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them. | 2 | 3 | 3 | 2 | 2 | ||||||||
| 12) Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms. | 3 | 3 | 3 | 3 | 2 | ||||||||
| 13) It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field. | 3 | 3 | 3 | 3 | 3 | ||||||||
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering. | 3 |
| 2) | Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming. | 3 |
| 3) | Uses different information technologies and systems for understanding and solving various business problems. | 3 |
| 4) | Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods. | 3 |
| 5) | Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database. | 3 |
| 6) | Gains technical and managerial contributions to IT projects and takes responsibility. | 3 |
| 7) | Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively. | 3 |
| 8) | Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education. | 2 |
| 9) | Develops teamwork, negotiation, leadership and entrepreneurship skills. | 3 |
| 10) | Has universal ethical values, social responsibility awareness and sufficient legal knowledge. | 3 |
| 11) | Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them. | 2 |
| 12) | Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms. | 3 |
| 13) | It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field. | 3 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Application | 2 | % 25 |
| Homework Assignments | 2 | % 25 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 14 | 2 | 2 | 56 | |||
| Application | 2 | 3 | 1 | 8 | |||
| Study Hours Out of Class | 14 | 2 | 28 | ||||
| Homework Assignments | 2 | 3 | 1 | 8 | |||
| Final | 1 | 20 | 1 | 21 | |||
| Total Workload | 121 | ||||||