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ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| COMPUTER ENGINEERING (PhD) (ENGLISH) | |
| Code | CENG013 |
| Name | Deep Learning |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi BUSE MELİS ÖZYILDIRIM |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
To be able to comprehend, apply and analyze deep learning algorithms which have wide usage area and which are successful artificial intelligence systems.
Course Content
It includes theoretical analysis of deep learning algorithms, implementation on sample data sets and analysis of results
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Comprehends the working principles of deep algorithms |
| LO02 | Learns to choose the appropriate algorithm for the problem |
| LO03 | Implements the algorithms on different datasets |
| LO04 | Learns to make hyperparameter settings |
| LO05 | Learns to analyze the results |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to machine learning and deep learning algorithms | Research on the subject | |
| 2 | Examination of the methods used in the performance measurement of algorithms | Research on the subject | |
| 3 | Description of 2 dimensional convolution networks | Research on the subject | |
| 4 | Description of 3 dimensional convolution networks | Research on the subject | |
| 5 | Determining hyperparameters of convolution networks | Testing convolutional networks on data set | |
| 6 | Discussion of transfer learning and fine tuning methods | Research on the subject | |
| 7 | Describing autoencoder algorithm | Research on the subject | |
| 8 | Mid-Term Exam | Reading the notes | |
| 9 | Analysis of autoencoders | Research on the subject | |
| 10 | Describing generative adversarial networks | Research on the subject | |
| 11 | Implementing generative adversarial networks | Programming generative adversarial networks | |
| 12 | Describing deep belief networks | Research on the subject | |
| 13 | Analyzing deep network structures used in segmentation application | Research on the subject | |
| 14 | Use of deep learning algorithms on audio data | Research on the subject | |
| 15 | Use of deep learning algorithms on text data | Research on the subject | |
| 16 | Term Exams | Reading the notes | |
| 17 | Term Exams | Reading notes |