Skip to content

AIC313: Introduction to Generative Models

Minhyuk Sung, KAIST, Fall 2026


Teaser1

Time & Location

Time: Mon/Wed 10:30 a.m. - 11:45 a.m. (KST)
Location: E3-5 Room 210

Description

Generative models aim to learn complex data distributions and generate new samples, providing the foundation for a wide range of applications in computer vision, computer graphics, natural language processing, and scientific discovery. With the rapid development of deep learning, diverse generative modeling approaches have emerged, each offering distinct perspectives on learning and sampling from data distributions. In this course, we will cover the fundamental principles and recent advances in generative models, including autoregressive models, variational autoencoders, generative adversarial networks, normalizing flows, diffusion and score-based models, and flow matching. We will also discuss conditional generation, inference-time guidance, practical applications, and remaining challenges in the field.

Prerequisites

  • Solid background in machine learning and deep learning
  • Hands-on experience with neural network implementation
  • Recommended prior courses:
    • MAS.20050 Probability and Statistics
    • MAS.20001 Differential Equations and Applications
    • CS.30701 Introduction to Deep Learning

Course Staff

Instructor: Minhyuk Sung (mhsung@kaist.ac.kr)

Grading

  • Participation & Quizzes: 15%
  • Exams: 45%
  • FastGen Challenge: 20%
  • CreativeGen Challenge: 20%

Important Dates

ALL ASSIGNMENTS ARE DUE 23:59 KST.

(Subject to Change)

  • Project Team Sign-Up: Due Sep 28 (Mon)
  • FastGen Challenge Submission: Due Nov 14 (Sat)
  • CreativeGen Challenge Submission: Due Dec 06 (Sat)

Schedule

(Subject to Change)

Week Mon Topic Wed Topic
1 Aug 31 Course Introduction Sep 02 Backgrounds
2 Sep 07 Autoregressive Models 1 Sep 09 Autoregressive Models 2
3 Sep 14 Maximum Likelihood Learning Sep 16 Variational Autoencoders 1
4 Sep 21 Variational Autoencoders 2 Sep 23 No Class (Break)
5 Sep 28 Generative Adversarial Networks 1 Sep 30 Generative Adversarial Networks 2
6 Oct 05 No Class (Substitute Holiday for National Foundation Day) Oct 07 Normalizing Flows 1
7 Oct 12 Normalizing Flows 2 Oct 14 Midterm Wrap-Up
8 Oct 19 No Class (Midterm Week) Oct 21 No Class (Midterm Week)
9 Oct 26 Diffusion Models 1 Oct 28 Diffusion Models 2
10 Nov 02 Diffusion Models 3 Nov 04 Score-Based Models
11 Nov 09 Conditional Generation /
Latent Diffusion		 Nov 11 Flow Matching 1
12 Nov 16 Flow Matching 2 Nov 18 Inference-Time Guidance 1
13 Nov 23 Inference-Time Guidance 2 Nov 25 Course Wrap-Up
14 Nov 30 Guest Lecture 1 Dec 02 Guest Lecture 2
15 Dec 07 Project Presentations 1 Dec 09 Project Presentations 2
16 Dec 14 No Class (Final Week) Dec 16 No Class (Final Week)

AI Coding Assistant Tool Policy

You are allowed (and even encouraged) to utilize AI coding assistant tools, such as ChatGPT, Copilot, Codex, and Code Intelligence, for your programming assignments and projects. Utilizing AI coding assistant tools will not be deemed as plagiarism. However, it is still strictly prohibited to directly copy code from the Internet or from someone else. Doing so will lead to a score of zero and a report to the university.


  1. AI-generated using GPT
     ↩