TÜRKÇE
ENGLISH
Information
Course Goal / Objective
In this course, attendees will: Understand the context of neural networks and deep learning Know how to use a neural network Understand the data needs of deep learning Have a working knowledge of neural networks and deep learning Explore the parameters for neural networks
Course Content
You do not need an extensive math background to understand neural network. In understandable steps, this course builds from a one node neural network to a multiple features, multiple output neural networks. All the steps are explained using working code to solve problems. After an understanding of how neural networks work and the parameters that control deep learning systems, Keras is introduced and used to simplify the building of deep learning neural networks. A convolutional deep learning neural network is built using Keras to show how deep learning is used in specialized neural networks. This course provides the necessary required background to understand ROI’s Time Series Analysis and Natural Language Processing courses.
Course Precondition
None
Resources
Y. Bengio, I. Goodfellow and A. Courville, “Deep Learning”, MIT Press, 2016.
Notes
Some papers
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Students can evaluate common deep learning methods in terms of effectiveness. |
| LO02 | Students can evaluate the advantages and disadvantages of the deep learning method that is considered to be used. |
| LO03 | Students can design and test basic deep learning solutions. |
| LO04 | Students identify and apply the appropriate deep learning architecture and algorithm for the predicted solution. |
| LO05 | Students have knowledge about editing and optimization methods of deep models. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 3 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 2 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 4 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 2 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 2 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 2 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 3 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction – History and Theoretical Foundations | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 2 | Mathematical Fundamentals: Linear Algebra, Probability and Information Theory | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 3 | Artificial Neural Networks Fundamentals | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 4 | Feed Forward Deep Networks | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 5 | Editing Deep or Distributed Models | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 6 | Optimization Techniques for Training Deep Models | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 7 | Convolutional Networks | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | All courses must be studied | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Autoencoders 1 | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 10 | Autoencoders 2 | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 11 | Linear Factor Models | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 12 | Learning through Representation-1 | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 13 | Learning through Representation-2 | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 14 | Deep Productive Models | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 15 | Boltzman Machines | Study to lecture notes and applications | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | All courses must be studied | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım |
| 17 | Term Exams | All courses must be studied | 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 | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||