Information Package / Course Catalogue
| Information Technologies (English) (Thesis) | |||||
| Master | Length of the Programme: 2 | Number of Credits: 120 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Graduate School | ||||
| Course Code | ITC 546 | ||||
| Course Title in English | Deep Learning | ||||
| Course Title in Turkish | Deep Learning | ||||
| Language of Instruction | EN | ||||
| Type of Course | Exercise,Flipped Classroom,Lecture | ||||
| Level of Course | Intermediate | ||||
| Semester | Summer School | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 175 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Basic machine learning knowledge | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only graduate Students | ||||
| Overall Educational Objective | To learn the machine learning concepts with on hands applications using modern tools to process data via tensorflow to drive conclusions using data analysis tools. | ||||
| Course Description | The aim of the course is to give the fundamentals of deep learning. This course introduces the fundamental framework in deep learning with the use of tensorflow. | ||||
| Course Description in Turkish | Bu dersin amacı derin öğrenmesi temellerini sağlamaktır. Bu derste tensorflow kullanımıyla makine öğrenmedeki temel çerçeve sunulacaktır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Recognize and discuss the contributions of deep learning at a medium level 2) Understand and apply the philosophy behind deep learning 3) Develop first at an idea level then as a practical manner a deep learning system 4) Evaluate the mechanism of a given ML system and further develop it in terms of performance |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1) An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | ||||
| 2) An ability to apply scientific and practical knowledge in statistics, computing and computer science. | ||||
| 3) A Comprehensive knowledge of analysis and modeling methods and their limitations. | ||||
| 4) An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | ||||
| 5) An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | ||||
| 6) An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | ||||
| 7) An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | ||||
| 8) An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | ||||
| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | ||||
| 10) An understanding the social and environmental aspects of IT applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to develop and deepen their knowledge in the field of Information Technologies at the level of expertise based on their undergraduate level qualifications. | N | |
| 2) | An ability to apply scientific and practical knowledge in statistics, computing and computer science. | N | |
| 3) | A Comprehensive knowledge of analysis and modeling methods and their limitations. | N | |
| 4) | An ability to design and apply analytical, modeling and experimental H 2 based researches, analyzes and interprets complex situations encountered in this process. | N | |
| 5) | An ability to transmit the process and results of the work of information systems systematically and clearly in written and oral form in national and international environments. | N | |
| 6) | An understanding of data collection, processing, use, interpretation and social, scientific and ethical values in all professional and professional activities. | N | |
| 7) | An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take responsibility. | N | |
| 8) | An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. | N | |
| 9) | An ability to communicate verbally and in writing in English at least at the level of B2 of CEFR. | N | |
| 10) | An understanding the social and environmental aspects of IT applications. | N |
| Prepared by and Date | , |
| Course Coordinator | TUNA ÇAKAR |
| Semester | Summer School |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week | Subject |
| 1) | What is deep learning? |
| 2) | Introduction to Fundamental Concepts in DL |
| 3) | Basic Mathematics of Deep Leaning |
| 4) | Advanced Mathematics of Deep Learning |
| 5) | Getting Started with Neural Networks |
| 6) | Fundamentals of Machine Learning |
| 7) | Deep Learning for Computer Vision I |
| 7) | Deep Learning for Computer Vision I |
| 8) | Deep Learning for Computer Vision II |
| 9) | Deep Learning for Text and Sequences I |
| 10) | Deep Learning for Text and Sequences II |
| 11) | Advanced Learning Best Practices I |
| 12) | Advanced Learning Best Practices II |
| 13) | Generative Deep Learning I |
| 14) | Generative Deep Learning II |
| 15) | final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/toolkit | ||||||||||||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | ||||||||||||||||||
| Homework and Projects | Announced | ||||||||||||||||||
| Laboratory Work | Each week there will be a lab session | ||||||||||||||||||
| Computer Use | Students will apply the methods they learned | ||||||||||||||||||
| Other Activities | None | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
cakart@mef.edu.tr 02123953600 Course Instructor: Asst. Prof. Tuna Çakar office: A-552 (A block, 5th floor) office hours via appointment by e-mail Grading and evaluation: Evaluation will be based on the student learning outcomes. Academic integrity: All students of MEF University are expected to be honest and comply with academic integrity. Students are expected to do their own work and neither give nor receive unauthorized assistance. Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54. You MUST attend the lab sessions to be able to submit your lab work. |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 2 | 1.5 | 49 | |||
| Laboratory | 14 | 2 | 1.5 | 49 | |||
| Project | 9 | 2 | 1 | 27 | |||
| Midterm(s) | 1 | 30 | 1 | 31 | |||
| Final Examination | 1 | 30 | 3 | 33 | |||
| Total Workload | 189 | ||||||
| Total Workload/25 | 7.6 | ||||||
| ECTS | 7.5 | ||||||