CENG0058 Applications of deep generative models

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG0058
Name Applications of deep generative models
Term 2023-2024 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Mehmet SARIGÜL


Course Goal / Objective

The goal of the Applications of Deep Generative Models course is to provide students with a comprehensive understanding of deep generative models and their diverse applications across various domains. The course aims to equip students with the knowledge and skills necessary to apply deep generative models to solve real-world problems, generate realistic synthetic data, and explore creative applications in areas such as computer vision, natural language processing, healthcare, and more.

Course Content

This course covers the Introduction to Deep Generative Models, Introduction to deep generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and flow-based models, Applications of VAEs in image generation, image-to-image translation, and data synthesis, Generative Adversarial Networks (GANs), GAN architecture and training techniques, GAN evaluation metrics (e.g., inception score, Frechet Inception Distance), Applications of GANs in image generation, style transfer, and data augmentation, Flow-Based Generative Models, Architecture and training of flow-based models, Density estimation and likelihood evaluation, Applications of flow-based models in image generation and synthesis, Text Generation and Language Modeling, Recurrent Neural Networks (RNNs) and LSTM models for language modeling, Applications of deep generative models in text generation, dialogue systems, and language translation, Unsupervised and Semi-supervised Learning, Anomaly Detection and Outlier Analysis, Applications in fraud detection, cybersecurity, and outlier analysis, Data Augmentation and Privacy, Healthcare and Medical Image Analysis, Artistic image generation using deep generative models, Ethical Considerations and Social Impact Advanced Topics and Recent Advancements.

Course Precondition

Knowledge of basic programming, linear algebra, and probability theory.

Resources

Tomczak, J. M. (2022). Deep generative modeling (pp. 1-197). Springer.

Notes

Tomczak, J. M. (2022). Deep generative modeling (pp. 1-197). Springer.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Understanding of Deep Generative Models
LO02 Knowledge of Generative Model Applications
LO03 Implementation skill for Domain-Specific Applications of Deep Generative Models
LO04 Evaluation and Assessment for Deep Learning Applications


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. 4
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. 4
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 3
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. 3
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. 2
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. 2
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 3
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. 1
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 1
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities. 2


Week Plan

Week Topic Preparation Methods
1 Introduction to deep generative models and their applications. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
2 Variational Autoencoders (VAEs): architecture, training, and applications. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
3 Generative Adversarial Networks (GANs): architecture, training, and applications. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
4 Flow-based generative models: architecture, training, and applications. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
5 Image generation and synthesis using deep generative models. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
6 Text generation and language modeling with deep generative models. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
7 Unsupervised and semi-supervised learning using generative models. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam Ölçme Yöntemleri:
Yazılı Sınav
9 Evaluation metrics for generative models: inception score, FID, etc. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
10 Anomaly detection and outlier analysis with generative models. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
11 Data augmentation using generative models. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
12 Privacy considerations and adversarial attacks on generative models. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
13 Healthcare and medical image analysis applications. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
14 Creative applications of generative models: art, music, design. Reading the lecture notes Öğretim Yöntemleri:
Anlatım
15 Review Reading the lecture notes Öğretim Yöntemleri:
Anlatım
16 Term Exams Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams 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 14 14
Final Exam 1 28 28
Total Workload (Hour) 154
Total Workload / 25 (h) 6,16
ECTS 6 ECTS

Update Time: 16.06.2023 02:36