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Information
| Code | CENG0056 |
| Name | Deep Generative Models |
| Term | 2024-2025 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 | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Mehmet SARIGÜL |
Course Goal / Objective
The goal of a Deep Generative Models course is to provide students with a comprehensive understanding of generative modeling techniques using deep learning architectures. Deep generative models aim to learn and generate new data samples that resemble a given dataset, capturing its underlying distribution and structure.
Course Content
This course covers the Introduction to Generative Modeling, Overview of generative modeling, Probabilistic modeling and likelihood estimation, Maximum Likelihood Estimation (MLE), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive Models, Deep Generative Models in Natural Language Processing, Advanced Topics in Deep Generative Models, Applications of Deep Generative Models, Critiquing Research Papers.
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 Generative Modeling |
| LO02 | Familiarity with Deep Learning Architectures |
| LO03 | Ability to Train and Evaluate Deep Generative Models |
| LO04 | Application of Deep Generative 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. | 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. | 3 |
| 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. | 2 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 3 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 3 |
| 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. | 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. | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. | 2 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to generative modeling, probabilistic modeling, and likelihood estimation. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Autoencoders and their limitations, introduction to Variational Autoencoders (VAEs). | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Variational inference, evidence lower bound (ELBO), and training VAEs. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Evaluation of VAEs, sampling from the latent space, and reconstruction quality. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Introduction to Generative Adversarial Networks (GANs) and the generator-discriminator framework. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | GAN training and the GAN objective, variations of GANs (e.g., DCGAN, WGAN). | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Challenges in GAN training (mode collapse, instability) and solutions. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Autoregressive models, PixelCNN, and PixelRNN. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Normalizing Flows and flow-based generative models. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Language modeling with deep generative models, text generation using RNNs and transformers. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Conditional generation and text-to-image synthesis. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Disentangled representation learning and its applications. | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Diffusion Models. | 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 | ||