| 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 |
Yaz Okulu |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 2+1 |
Other: 0 |
|
| Estimated Student Workload |
175 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
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| 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 Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Derin öğrenmenin orta düzeydeki katkılarını tanımak ve tartışmak
2) Derin öğrenmenin arkasındaki felsefeyi anlamak ve uygulamak
3) Önce fikir düzeyinde, sonra pratik olarak derin bir öğrenme sistemi geliştirmek
4) Belirli bir ML sisteminin mekanizmasını değerlendirin ve performans açısından daha da geliştirmek
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| Program Learning Outcomes/Course Learning Outcomes |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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Program Outcomes and Competences |
Level |
Assessed by |
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| Prepared by and Date |
, |
| Course Coordinator |
TUNA ÇAKAR |
| Semester |
Yaz Okulu |
| Name of Instructor |
Dr. Öğr. Üyesi TUNA ÇAKAR |
Course Contents
| Hafta |
Konu |
| 1) |
Derin öğrenme nedir? |
| 2) |
DL'de Temel Kavramlara Giriş |
| 3) |
Derin Öğrenmenin Temel Matematiği
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| 4) |
Derin Öğrenmenin İleri Matematiği |
| 5) |
Sinir Ağlarına Başlangıç |
| 6) |
Makine Öğreniminin Temelleri |
| 7) |
Bilgisayarla Görme için Derin Öğrenme I
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| 7) |
Bilgisayarla Görme için Derin Öğrenme I
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Bilgisayarla Görme için Derin Öğrenme II |
| 9) |
Metin ve Diziler için Derin Öğrenme I
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| 10) |
Metin ve Diziler için Derin Öğrenme II |
| 11) |
Gelişmiş Öğrenme En İyi Uygulamaları I
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| 12) |
Gelişmiş Öğrenme En İyi Uygulamaları II |
| 13) |
Üretken Derin Öğrenme I |
| 14) |
Üretken Derin Öğrenme II |
| 15) |
Proje/sunum Dönemi |
| 16) |
Proje/Sunum Dönemi |
| 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 |
| Assessment Tools |
Count |
Weight |
| Laboratuar |
5 |
% 25 |
| Küçük Sınavlar |
5 |
% 25 |
| Projeler |
1 |
% 25 |
| Final |
1 |
% 25 |
| TOTAL |
% 100 |
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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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Bilgi Paketi / Ders Kataloğu