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Information
| Code | YZ010 |
| Name | Deep Learning |
| Term | 2024-2025 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
Dr. Öğr. Üyesi Mehmet SARIGÜL
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Students become familiar with deep learning models and gain the ability to use them
Course Content
The increasing application of deep learning models and their effectiveness in solving different mathematical problems constitute the content of this course.
Course Precondition
Intermediate python knowledge
Resources
Eli Stevens, Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools Manning, 2020, 9781617295263
Notes
Eli Stevens, Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools Manning, 2020, 9781617295263
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Students will be able to use artificial neural networks in natural language processing in real world applications |
| LO02 | Students understand the basic concepts of deep learning and comprehend the working principles of artificial neural networks |
| LO03 | Students will be able to use artificial neural networks in image processing in real world applications |
| LO04 | Students understand the basic concepts of deep learning and can use them in applications |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Beceriler - Bilişsel, Uygulamalı | To be able to access information broadly and deeply by conducting scientific research in the field, to be able to evaluate, interpret and apply the information. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has a comprehensive knowledge of current techniques and methods applied in engineering and their limitations. | 5 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | To be able to use uncertain, limited or incomplete data to complete and apply knowledge using scientific methods; to be able to use knowledge from different disciplines together. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Is aware of new and emerging practices of the profession, examines and learns them when needed. | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions. | 4 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or original ideas and methods; designs complex systems or processes and develops innovative/alternative solutions in their designs. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to work effectively in disciplinary and multidisciplinary teams, to lead such teams and to develop solution approaches in complex situations; to be able to work independently and take responsibility. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | To be able to communicate orally and in writing in a foreign language at least at the B2 level of the European Language Portfolio. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | To be able to communicate the process and results of his/her studies systematically and clearly in written or oral form in national and international environments in or outside the field. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Knows the social, environmental, health, safety, legal, project management and business life practices of engineering applications and is aware of the constraints these impose on engineering applications. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction and Fundamentals of Deep Learning | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 2 | Introduction and Fundamentals of Deep Learning 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 3 | Deep Artificial Neural Networks | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 4 | Deep Artificial Neural Networks 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 5 | Advanced Deep Learning Techniques | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 6 | Advanced Deep Learning Techniques 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 7 | Sequential Modeling and Applications | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Sequential Modeling and Applications 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 10 | Accelerated Deep Learning | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 11 | Accelerated Deep Learning 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 12 | Automatic Learning and Hyperparameterization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 13 | Automatic Learning and Hyperparameterization 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 14 | Application and Project Presentations | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 15 | Application and Project Presentations 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 162 | ||
| Total Workload / 25 (h) | 6,48 | ||
| ECTS | 6 ECTS | ||